{ "schema": "modelrater-leaderboard/3", "portal": "https://mdata.modelraters.com", "updated_at": "2026-10-27T20:17:52.297209", "total_models": 10858, "leaderboard": [ { "rank": 1, "dossier_id": "deepseek/v4/v4-1-flash", "name": "DeepSeek-V4.1-Flash", "maker": "deepseek", "family": "deepseek/v4", "context_length": 1000000, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "active", "weights": "open-weights", "empirical_score": 67, "axes_scored": 5, "cautious_projection": 66, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2, "dossier_id": "xai/grok/grok-4-5", "name": "Grok 4.5", "maker": "xai", "family": "xai/grok", "context_length": 500000, "prompt_price_per_m": 2.0, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": 64, "axes_scored": 5, "cautious_projection": 63, "high_projection": 69, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3, "dossier_id": "anthropic/sonnet/claude-sonnet-4", "name": "Claude Sonnet 4", "maker": "anthropic", "family": "anthropic/sonnet", "context_length": 200000, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "retired", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4, "dossier_id": "deepseek/chat/deepseek-chat", "name": "deepseek-chat", "maker": "deepseek", "family": "deepseek/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": 1315, "coding_elo": 1320, "swe_bench": 36.2, "humaneval": 89.4, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1084, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5, "dossier_id": "inception/mercury/mercury-2", "name": "Mercury 2", "maker": "inception", "family": "inception/mercury", "context_length": 128000, "prompt_price_per_m": 0.25, "completion_price_per_m": 0.75, "status": "deprecated", "weights": "closed", "empirical_score": 63, "axes_scored": 3, "cautious_projection": 58, "high_projection": 74, "lmsys_elo": 1211, "coding_elo": 1211, "swe_bench": 16.9, "humaneval": 71.5, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": 10.8, "aa_coding_index": 31.1, "aa_agentic_index": 1.6, "design_arena_elo": 975, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6, "dossier_id": "anthropic/opus/claude-opus-4-7", "name": "Claude Opus 4.7", "maker": "anthropic", "family": "anthropic/opus", "context_length": 1000000, "prompt_price_per_m": 5.0, "completion_price_per_m": 25.0, "status": "active", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7, "dossier_id": "anthropic/opus/claude-opus-4-8", "name": "Claude Opus 4.8", "maker": "anthropic", "family": "anthropic/opus", "context_length": 1000000, "prompt_price_per_m": 5.0, "completion_price_per_m": 25.0, "status": "active", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8, "dossier_id": "nvidia/nemotron/nemotron-3-super", "name": "Nemotron 3 Super", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 63, "axes_scored": 6, "cautious_projection": 63, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "NVIDIA positions Super for collaborative agents and workloads such as IT ticket automation, tool use, and long-context reasoning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9, "dossier_id": "xai/grok/grok-4-3", "name": "Grok 4.3", "maker": "xai", "family": "xai/grok", "context_length": 1000000, "prompt_price_per_m": 1.25, "completion_price_per_m": 2.5, "status": "active", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10, "dossier_id": "xai/grok/grok-build-0-1", "name": "Grok Build 0.1", "maker": "xai", "family": "xai/grok", "context_length": 256000, "prompt_price_per_m": 1.0, "completion_price_per_m": 2.0, "status": "active", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 11, "dossier_id": "anthropic/haiku/claude-haiku-4-5", "name": "Claude Haiku 4.5", "maker": "anthropic", "family": "anthropic/haiku", "context_length": 200000, "prompt_price_per_m": 1.0, "completion_price_per_m": 5.0, "status": "active", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 12, "dossier_id": "anthropic/opus/claude-opus-4-1", "name": "Claude Opus 4.1", "maker": "anthropic", "family": "anthropic/opus", "context_length": 200000, "prompt_price_per_m": 15.0, "completion_price_per_m": 75.0, "status": "retired", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 13, "dossier_id": "anthropic/opus/claude-opus-4-5", "name": "Claude Opus 4.5", "maker": "anthropic", "family": "anthropic/opus", "context_length": 200000, "prompt_price_per_m": 5.0, "completion_price_per_m": 25.0, "status": "active", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 14, "dossier_id": "anthropic/sonnet/claude-sonnet-4-5", "name": "Claude Sonnet 4.5", "maker": "anthropic", "family": "anthropic/sonnet", "context_length": 200000, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "deprecated", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 15, "dossier_id": "deepseek/v3/v3", "name": "DeepSeek-V3", "maker": "deepseek", "family": "deepseek/v3", "context_length": 128000, "prompt_price_per_m": 0.27, "completion_price_per_m": 1.1, "status": "deprecated", "weights": "open-weights", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 16, "dossier_id": "deepseek/v3/v3-2-exp", "name": "DeepSeek-V3.2-Exp", "maker": "deepseek", "family": "deepseek/v3", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 17, "dossier_id": "xai/grok/grok-4-20", "name": "Grok 4.20", "maker": "xai", "family": "xai/grok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 63, "axes_scored": 4, "cautious_projection": 60, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 18, "dossier_id": "xai/grok/grok-4-6", "name": "Grok 4.6", "maker": "xai", "family": "xai/grok", "context_length": 500000, "prompt_price_per_m": 2.0, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": 61, "axes_scored": 5, "cautious_projection": 60, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 19, "dossier_id": "google/gemma/gemma-2-27b", "name": "Gemma 2 27B", "maker": "google", "family": "google/gemma", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 61, "axes_scored": 5, "cautious_projection": 60, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation, summarization, and extraction.", "summary_intro": "Google DeepMind open-weights Gemma 2 text model. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 20, "dossier_id": "inclusionai/ling/ling-3-0-flash-sante", "name": "Ling 3.0 Flash Sante", "maker": "inclusionai", "family": "inclusionai/ling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 61, "axes_scored": 5, "cautious_projection": 60, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 21, "dossier_id": "upstage/solar/solar-pro-4", "name": "Solar Pro 4", "maker": "upstage", "family": "upstage/solar", "context_length": 524288, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "active", "weights": "closed", "empirical_score": 60, "axes_scored": 6, "cautious_projection": 60, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 22, "dossier_id": "upstage/solar/solar-mini-4", "name": "Solar Mini 4", "maker": "upstage", "family": "upstage/solar", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.4, "status": "active", "weights": "closed", "empirical_score": 60, "axes_scored": 6, "cautious_projection": 60, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Upstage says it is for high-volume agent work where speed and cost matter, with Korean, English, and Japanese.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 23, "dossier_id": "tencent/hunyuan/hunyuan-a13b", "name": "Hunyuan-A13B", "maker": "tencent", "family": "tencent/hunyuan", "context_length": 262144, "prompt_price_per_m": 0.5, "completion_price_per_m": 2.0, "status": "active", "weights": "open-weights", "empirical_score": 59, "axes_scored": 2, "cautious_projection": 52, "high_projection": 74, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tencent presents it as an efficient general model for math, science, coding, long context, and tool-using agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 24, "dossier_id": "deepseek/v3/v3-1", "name": "DeepSeek-V3.1", "maker": "deepseek", "family": "deepseek/v3", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": 59, "axes_scored": 4, "cautious_projection": 58, "high_projection": 69, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 25, "dossier_id": "anthropic/opus/claude-opus-5", "name": "Claude Opus 5", "maker": "anthropic", "family": "anthropic/opus", "context_length": 1000000, "prompt_price_per_m": 5.0, "completion_price_per_m": 25.0, "status": "active", "weights": "closed", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 78.4, "speed_tps": 120.0, "cosmos_tests": 18, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 50.8, "aa_coding_index": 78.0, "aa_agentic_index": 56.5, "design_arena_elo": 1225, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 26, "dossier_id": "anthropic/sonnet/claude-sonnet-5", "name": "Claude Sonnet 5", "maker": "anthropic", "family": "anthropic/sonnet", "context_length": 1000000, "prompt_price_per_m": 2.0, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 78.4, "speed_tps": 120.0, "cosmos_tests": 18, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 38.2, "aa_coding_index": 71.5, "aa_agentic_index": 43.6, "design_arena_elo": 1190, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 27, "dossier_id": "deepseek/v4/v4-flash-vision-exp", "name": "DeepSeek-V4-Flash-Vision-Exp", "maker": "deepseek", "family": "deepseek/v4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.6, "humaneval": 71.3, "twelve_cs_score": 31.7, "speed_tps": 240.0, "cosmos_tests": 49, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 34.8, "aa_coding_index": 65.0, "aa_agentic_index": 47.5, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 28, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5", "name": "MiMo-V2.5", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": 1048576, "prompt_price_per_m": 0.14, "completion_price_per_m": 0.28, "status": "deprecated", "weights": "open-weights", "empirical_score": 58, "axes_scored": 3, "cautious_projection": 55, "high_projection": 71, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs_score": 67.9, "speed_tps": 120.0, "cosmos_tests": 36, "is_free": false, "what_it_is_for": "Xiaomi says it is the cheaper omni model for agent work and native image, video, and audio understanding.", "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.", "benchmarks": { "aa_intelligence_index": 21.8, "aa_coding_index": 56.8, "aa_agentic_index": 15.8, "design_arena_elo": 1213, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 29, "dossier_id": "google/gemini/gemini-2-5-flash-lite", "name": "Gemini 2.5 Flash-Lite", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.4, "status": "active", "weights": "closed", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google calls it the fastest, cheapest multimodal model in the 2.5 family, for classification, extraction, and low-latency work.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 30, "dossier_id": "anthropic/sonnet/claude-sonnet-4-6", "name": "Claude Sonnet 4.6", "maker": "anthropic", "family": "anthropic/sonnet", "context_length": 1000000, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "active", "weights": "closed", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 31, "dossier_id": "anthropic/sonnet/claude-sonnet-5-5", "name": "Claude Sonnet 5.5", "maker": "anthropic", "family": "anthropic/sonnet", "context_length": 1000000, "prompt_price_per_m": 2.0, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 32, "dossier_id": "nvidia/nemotron/nemotron-3-nano", "name": "Nemotron 3 Nano", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 33, "dossier_id": "ibm/granite/granite-4-0-h-micro", "name": "Granite-4.0-H-Micro", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 34, "dossier_id": "meta/llama/llama-3-3-70b", "name": "Llama 3.3 70B", "maker": "meta", "family": "meta/llama", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 35, "dossier_id": "mistral/mistral/mistral-small-3-0-25-01", "name": "Mistral Small 3.0", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "open-weights", "empirical_score": 58, "axes_scored": 5, "cautious_projection": 58, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A small efficient and powerfull 24B open model for personal or commercial use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 36, "dossier_id": "anthropic/fable/claude-fable-5", "name": "Claude Fable 5", "maker": "anthropic", "family": "anthropic/fable", "context_length": 1000000, "prompt_price_per_m": 10.0, "completion_price_per_m": 50.0, "status": "active", "weights": "closed", "empirical_score": 56, "axes_scored": 4, "cautious_projection": 55, "high_projection": 66, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 20.4, "speed_tps": 120.0, "cosmos_tests": 18, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 49.6, "aa_coding_index": 76.5, "aa_agentic_index": 50.7, "design_arena_elo": 1249, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 37, "dossier_id": "xai/grok/grok-4-20-multi-agent", "name": "Grok 4.20 Multi-Agent", "maker": "xai", "family": "xai/grok", "context_length": 1000000, "prompt_price_per_m": 1.25, "completion_price_per_m": 2.5, "status": "preview", "weights": "closed", "empirical_score": 56, "axes_scored": 4, "cautious_projection": 55, "high_projection": 66, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 38, "dossier_id": "anthropic/fable/claude-fable-5-1", "name": "Claude Fable 5.1", "maker": "anthropic", "family": "anthropic/fable", "context_length": 1000000, "prompt_price_per_m": 10.0, "completion_price_per_m": 50.0, "status": "active", "weights": "closed", "empirical_score": 56, "axes_scored": 4, "cautious_projection": 55, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 39, "dossier_id": "deepseek/r1/r1", "name": "DeepSeek-R1", "maker": "deepseek", "family": "deepseek/r1", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": 56, "axes_scored": 4, "cautious_projection": 55, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 40, "dossier_id": "inclusionai/ling/ling-3-0-flash-fin", "name": "Ling 3.0 Flash Fin", "maker": "inclusionai", "family": "inclusionai/ling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 56, "axes_scored": 4, "cautious_projection": 55, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 41, "dossier_id": "inception/mercury/mercury-2-5", "name": "Mercury 2.5", "maker": "inception", "family": "inception/mercury", "context_length": 260000, "prompt_price_per_m": 0.04, "completion_price_per_m": 0.15, "status": "active", "weights": "closed", "empirical_score": 55, "axes_scored": 5, "cautious_projection": 55, "high_projection": 60, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.3, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 12.3, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 42, "dossier_id": "mistral/ministral/ministral-3-3b-25-12", "name": "Ministral 3 3B", "maker": "mistral", "family": "mistral/ministral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "open-weights", "empirical_score": 55, "axes_scored": 5, "cautious_projection": 55, "high_projection": 61, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.2, "humaneval": 70.2, "twelve_cs_score": 52.0, "speed_tps": 240.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 4.8, "aa_coding_index": 4.8, "aa_agentic_index": 0.8, "design_arena_elo": 960, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 43, "dossier_id": "xai/grok/grok-4-7", "name": "Grok 4.7", "maker": "xai", "family": "xai/grok", "context_length": 500000, "prompt_price_per_m": 2.0, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": 55, "axes_scored": 5, "cautious_projection": 55, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 44, "dossier_id": "cohere/north/north-mini-code-1-0", "name": "North Mini Code", "maker": "cohere", "family": "cohere/north", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": 55, "axes_scored": 6, "cautious_projection": 55, "high_projection": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 45, "dossier_id": "google/gemma/gemma-3-12b", "name": "Gemma 3 12B", "maker": "google", "family": "google/gemma", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 55, "axes_scored": 6, "cautious_projection": 55, "high_projection": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 46, "dossier_id": "ibm/granite/granite-4-2-8b", "name": "Granite-4.2-8B", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 55, "axes_scored": 5, "cautious_projection": 55, "high_projection": 61, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 47, "dossier_id": "amazon/nova/nova-micro", "name": "Amazon Nova Micro", "maker": "amazon", "family": "amazon/nova", "context_length": 128000, "prompt_price_per_m": 0.035, "completion_price_per_m": 0.14, "status": "active", "weights": "closed", "empirical_score": 55, "axes_scored": 6, "cautious_projection": 55, "high_projection": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 48, "dossier_id": "mistral/mistral/mistral-small-3-2-25-06", "name": "Mistral Small 3.2", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "open-weights", "empirical_score": 55, "axes_scored": 5, "cautious_projection": 55, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An update to our previous small model, released June 2025.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. An update to our previous small model, released June 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 49, "dossier_id": "mistral/voxtral/voxtral-small-25-07", "name": "Voxtral Small", "maker": "mistral", "family": "mistral/voxtral", "context_length": null, "prompt_price_per_m": 0.004, "completion_price_per_m": 0.4, "status": "active", "weights": "open-weights", "empirical_score": 55, "axes_scored": 5, "cautious_projection": 55, "high_projection": 61, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our first model with audio input capabilities for instruct use cases.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our first model with audio input capabilities for instruct use cases.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 50, "dossier_id": "prism-ml/ternary-bonsai/ternary-bonsai-2-27b", "name": "Ternary Bonsai 2 27B", "maker": "prism-ml", "family": "prism-ml/ternary-bonsai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 55, "axes_scored": 5, "cautious_projection": 55, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 51, "dossier_id": "inclusionai/ling/ling-3-1-flash", "name": "Ling 3.1 Flash", "maker": "inclusionai", "family": "inclusionai/ling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 54, "axes_scored": 3, "cautious_projection": 52, "high_projection": 69, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 52, "dossier_id": "meituan/longcat/longcat-2-0", "name": "LongCat-2.0", "maker": "meituan", "family": "meituan/longcat", "context_length": 1000000, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "active", "weights": "open-weights", "empirical_score": 53, "axes_scored": 2, "cautious_projection": 49, "high_projection": 71, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 53, "dossier_id": "openai/gpt-oss/gpt-oss-120b", "name": "gpt-oss-120b", "maker": "openai", "family": "openai/gpt-oss", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 52, "axes_scored": 6, "cautious_projection": 52, "high_projection": 52, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 18, "is_free": false, "what_it_is_for": "Most powerful open-weight model, fits into an H100 GPU", "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).", "benchmarks": { "aa_intelligence_index": 11.6, "aa_coding_index": 30.4, "aa_agentic_index": 3.7, "design_arena_elo": 898, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 54, "dossier_id": "tencent/hunyuan-mt/hy-mt2-30b-a3b", "name": "Hy-MT2-30B-A3B", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 52, "axes_scored": 4, "cautious_projection": 52, "high_projection": 63, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Tencent's strongest open MT2 checkpoint for general, business, domain, and instruction-following translation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 55, "dossier_id": "openai/gpt-oss/gpt-oss-20b", "name": "gpt-oss-20b", "maker": "openai", "family": "openai/gpt-oss", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 52, "axes_scored": 6, "cautious_projection": 52, "high_projection": 52, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.2, "humaneval": 70.2, "twelve_cs_score": 89.3, "speed_tps": 120.0, "cosmos_tests": 28, "is_free": false, "what_it_is_for": "Medium-sized open-weight model for low latency", "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).", "benchmarks": { "aa_intelligence_index": 10.0, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 933, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 56, "dossier_id": "anthropic/opus/claude-opus-4-6", "name": "Claude Opus 4.6", "maker": "anthropic", "family": "anthropic/opus", "context_length": 1000000, "prompt_price_per_m": 5.0, "completion_price_per_m": 25.0, "status": "active", "weights": "closed", "empirical_score": 52, "axes_scored": 5, "cautious_projection": 52, "high_projection": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 57, "dossier_id": "stepfun/step/step-3-5-flash", "name": "Step 3.5 Flash", "maker": "stepfun", "family": "stepfun/step", "context_length": 256000, "prompt_price_per_m": 0.7, "completion_price_per_m": 2.1, "status": "active", "weights": "open-weights", "empirical_score": 52, "axes_scored": 5, "cautious_projection": 52, "high_projection": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 58, "dossier_id": "google/gemma/gemma-3-27b", "name": "Gemma 3 27B", "maker": "google", "family": "google/gemma", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 52, "axes_scored": 6, "cautious_projection": 52, "high_projection": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 59, "dossier_id": "deepseek/v3/v3-2", "name": "DeepSeek-V3.2", "maker": "deepseek", "family": "deepseek/v3", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": 52, "axes_scored": 5, "cautious_projection": 52, "high_projection": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 60, "dossier_id": "minimax/m/minimax-m1", "name": "MiniMax-M1", "maker": "minimax", "family": "minimax/m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 52, "axes_scored": 5, "cautious_projection": 52, "high_projection": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 61, "dossier_id": "bytedance/seed/seed-2-0-mini", "name": "Seed 2.0 Mini", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": 2.0, "status": "active", "weights": "closed", "empirical_score": 51, "axes_scored": 5, "cautious_projection": 52, "high_projection": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 62, "dossier_id": "alibaba/qwen-coder/qwen3-coder-30b-a3b", "name": "Qwen3-Coder-30B-A3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 49, "axes_scored": 3, "cautious_projection": 49, "high_projection": 66, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs_score": 99.5, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Smaller open Qwen3 coder, a sparse instruct model.", "summary_intro": "Alibaba model Qwen3-Coder-30B-A3B-Instruct. Status: active. Collected 2026-10-15. Smaller open Qwen3 coder, a sparse instruct model.", "benchmarks": { "aa_intelligence_index": 9.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1085, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 63, "dossier_id": "alibaba/qwen/qwen3-32b", "name": "Qwen3-32B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 49, "axes_scored": 3, "cautious_projection": 49, "high_projection": 66, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs_score": 52.0, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "32B dense instruction-tuned Qwen3 model.", "summary_intro": "Alibaba model Qwen3-32B. Status: active. Collected 2026-10-15. 32B dense instruction-tuned Qwen3 model.", "benchmarks": { "aa_intelligence_index": 7.3, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 64, "dossier_id": "alibaba/qwen/qwen3-5-flash", "name": "Qwen3.5-Flash", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": 49, "axes_scored": 3, "cautious_projection": 49, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller hosted Qwen3.5 model. Snapshot qwen3.5-flash-2026-02-23 is listed on the vision page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 65, "dossier_id": "nvidia/nemotron/nemotron-3-5-lightning", "name": "Nemotron 3.5 Lightning", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 49, "axes_scored": 6, "cautious_projection": 49, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 66, "dossier_id": "amazon/nova/nova-lite", "name": "Amazon Nova Lite", "maker": "amazon", "family": "amazon/nova", "context_length": 300000, "prompt_price_per_m": 0.06, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 49, "axes_scored": 6, "cautious_projection": 49, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 67, "dossier_id": "nvidia/nemotron/nemotron-3-nano-omni", "name": "Nemotron 3 Nano Omni", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 49, "axes_scored": 6, "cautious_projection": 49, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 68, "dossier_id": "alibaba/qwen/qwen3-235b-a22b", "name": "Qwen3-235B-A22B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 49, "axes_scored": 3, "cautious_projection": 49, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest open Qwen3 mixture-of-experts model from April 2025.", "summary_intro": "Alibaba model Qwen3-235B-A22B. Status: active. Collected 2026-10-15. Largest open Qwen3 mixture-of-experts model from April 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 69, "dossier_id": "apodex/apodex/apodex-1-1-mini", "name": "Apodex 1.1 Mini", "maker": "apodex", "family": "apodex/apodex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 49, "axes_scored": 3, "cautious_projection": 49, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 70, "dossier_id": "inclusionai/ling/ling-3-0-flash", "name": "Ling 3.0 Flash", "maker": "inclusionai", "family": "inclusionai/ling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 49, "axes_scored": 6, "cautious_projection": 49, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 71, "dossier_id": "perceptron/perceptron/perceptron-mk1", "name": "Perceptron MK1", "maker": "perceptron", "family": "perceptron/perceptron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 49, "axes_scored": 3, "cautious_projection": 49, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 72, "dossier_id": "perceptron/perceptron/perceptron-mk1-5", "name": "Perceptron MK1.5", "maker": "perceptron", "family": "perceptron/perceptron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 49, "axes_scored": 3, "cautious_projection": 49, "high_projection": 66, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 73, "dossier_id": "deepseek/v4/v4-pro", "name": "DeepSeek-V4-Pro", "maker": "deepseek", "family": "deepseek/v4", "context_length": 1000000, "prompt_price_per_m": 1.32, "completion_price_per_m": 3.96, "status": "active", "weights": "open-weights", "empirical_score": 48, "axes_scored": 5, "cautious_projection": 49, "high_projection": 55, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.7, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 26, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 30.4, "aa_coding_index": 59.4, "aa_agentic_index": 26.3, "design_arena_elo": 948, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 74, "dossier_id": "tencent/hy/hy3", "name": "Hy3", "maker": "tencent", "family": "tencent/hy", "context_length": 262144, "prompt_price_per_m": 0.132, "completion_price_per_m": 0.528, "status": "active", "weights": "open-source", "empirical_score": 48, "axes_scored": 5, "cautious_projection": 49, "high_projection": 55, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs_score": 8.6, "speed_tps": 120.0, "cosmos_tests": 998, "is_free": false, "what_it_is_for": "Tencent positions Hy3 for reasoning, coding, tool use, and long-context agent work at a lower cost than Hy4 preview.", "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.", "benchmarks": { "aa_intelligence_index": 25.3, "aa_coding_index": 58.8, "aa_agentic_index": 24.1, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 75, "dossier_id": "alibaba/qwen/qwen3-30b-a3b", "name": "Qwen3-30B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 48, "axes_scored": 5, "cautious_projection": 49, "high_projection": 55, "lmsys_elo": 1207, "coding_elo": 1207, "swe_bench": 16.2, "humaneval": 71.0, "twelve_cs_score": 91.1, "speed_tps": 482.0, "cosmos_tests": 34, "is_free": false, "what_it_is_for": "Sparse April 2025 Qwen3 model. Later instruct and thinking 2507 checkpoints are priced separately on Model Studio.", "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.", "benchmarks": { "aa_intelligence_index": 7.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 940, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 76, "dossier_id": "tencent/hy/hy3", "name": "Hy3", "maker": "tencent", "family": "tencent/hy", "context_length": 262144, "prompt_price_per_m": 0.132, "completion_price_per_m": 0.528, "status": "active", "weights": "open-source", "empirical_score": 48, "axes_scored": 5, "cautious_projection": 49, "high_projection": 55, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.8, "humaneval": 70.7, "twelve_cs_score": 72.3, "speed_tps": 120.0, "cosmos_tests": 14, "is_free": false, "what_it_is_for": "Tencent positions Hy3 for reasoning, coding, tool use, and long-context agent work at a lower cost than Hy4 preview.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1173, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 77, "dossier_id": "tencent/hunyuan-mt/hy-mt2-7b", "name": "Hy-MT2-7B", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 48, "axes_scored": 4, "cautious_projection": 50, "high_projection": 61, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs_score": 28.2, "speed_tps": 120.0, "cosmos_tests": 12, "is_free": false, "what_it_is_for": "Higher-quality translation than the 1.8B model, including terminology, style, and structured-data instructions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 78, "dossier_id": "anthropic/opus/claude-opus-5-5", "name": "Claude Opus 5.5", "maker": "anthropic", "family": "anthropic/opus", "context_length": 1000000, "prompt_price_per_m": 4.0, "completion_price_per_m": 20.0, "status": "active", "weights": "closed", "empirical_score": 48, "axes_scored": 5, "cautious_projection": 49, "high_projection": 55, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 79, "dossier_id": "bytedance/seed/seed-1-6-flash", "name": "Seed 1.6 Flash", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": 48, "axes_scored": 4, "cautious_projection": 49, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 80, "dossier_id": "bytedance/ui-tars/ui-tars", "name": "UI-TARS", "maker": "bytedance", "family": "bytedance/ui-tars", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 48, "axes_scored": 4, "cautious_projection": 49, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 81, "dossier_id": "morph/morph/morph-v3-large", "name": "Morph V3 Large", "maker": "morph", "family": "morph/morph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 48, "axes_scored": 4, "cautious_projection": 50, "high_projection": 61, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 82, "dossier_id": "openai/gpt-oss/gpt-oss-safeguard-20b", "name": "gpt-oss-safeguard-20b", "maker": "openai", "family": "openai/gpt-oss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 48, "axes_scored": 4, "cautious_projection": 50, "high_projection": 61, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 83, "dossier_id": "anthropic/haiku/claude-haiku-5-5", "name": "Claude Haiku 5.5", "maker": "anthropic", "family": "anthropic/haiku", "context_length": 1000000, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.5, "status": "active", "weights": "closed", "empirical_score": 47, "axes_scored": 4, "cautious_projection": 49, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 84, "dossier_id": "tencent/hy/hy4-preview", "name": "Hy4 preview", "maker": "tencent", "family": "tencent/hy", "context_length": 1048576, "prompt_price_per_m": 0.834, "completion_price_per_m": 2.501, "status": "preview", "weights": "open-source", "empirical_score": 46, "axes_scored": 2, "cautious_projection": 47, "high_projection": 69, "lmsys_elo": 1233, "coding_elo": 1233, "swe_bench": 20.6, "humaneval": 74.5, "twelve_cs_score": 36.1, "speed_tps": 120.0, "cosmos_tests": 9, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 85, "dossier_id": "poolside/laguna/laguna-s-2-1", "name": "Laguna S 2.1", "maker": "poolside", "family": "poolside/laguna", "context_length": 1048576, "prompt_price_per_m": 0.09, "completion_price_per_m": 0.18, "status": "active", "weights": "open-weights", "empirical_score": 46, "axes_scored": 6, "cautious_projection": 46, "high_projection": 46, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.4, "twelve_cs_score": 89.3, "speed_tps": 120.0, "cosmos_tests": 28, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 86, "dossier_id": "openai/gpt/gpt-4-1-nano", "name": "GPT-4.1 nano", "maker": "openai", "family": "openai/gpt", "context_length": 1047576, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.4, "status": "deprecated", "weights": "closed", "empirical_score": 46, "axes_scored": 6, "cautious_projection": 46, "high_projection": 46, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fastest, most cost-efficient version of GPT-4.1", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 87, "dossier_id": "inclusionai/ling/ling-3-0-flash-vl", "name": "Ling 3.0 Flash VL", "maker": "inclusionai", "family": "inclusionai/ling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 46, "axes_scored": 6, "cautious_projection": 46, "high_projection": 46, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 88, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5-pro", "name": "MiMo-V2.5-Pro", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": 1048576, "prompt_price_per_m": 0.435, "completion_price_per_m": 0.87, "status": "deprecated", "weights": "open-weights", "empirical_score": 45, "axes_scored": 4, "cautious_projection": 47, "high_projection": 58, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.7, "twelve_cs_score": 41.2, "speed_tps": 120.0, "cosmos_tests": 70, "is_free": false, "what_it_is_for": "Xiaomi built it for long agent traces, software engineering, and tasks that stay coherent over hundreds of tool calls.", "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.", "benchmarks": { "aa_intelligence_index": 26.0, "aa_coding_index": 60.2, "aa_agentic_index": 21.3, "design_arena_elo": 1242, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 89, "dossier_id": "cohere/command/command-r7b-12-2024", "name": "Command R7B", "maker": "cohere", "family": "cohere/command", "context_length": 128000, "prompt_price_per_m": 0.0375, "completion_price_per_m": 0.15, "status": "active", "weights": "open-weights", "empirical_score": 45, "axes_scored": 5, "cautious_projection": 46, "high_projection": 52, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.3, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 18, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 90, "dossier_id": "meta/llama/llama-3-1-8b", "name": "Llama 3.1 8B", "maker": "meta", "family": "meta/llama", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 45, "axes_scored": 5, "cautious_projection": 46, "high_projection": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 91, "dossier_id": "meta/muse/muse-glimmer", "name": "Muse Glimmer", "maker": "meta", "family": "meta/muse", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": 45, "axes_scored": 4, "cautious_projection": 47, "high_projection": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 92, "dossier_id": "tencent/hunyuan-mt/hy-mt2-1-8b", "name": "Hy-MT2-1.8B", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 45, "axes_scored": 4, "cautious_projection": 47, "high_projection": 58, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 93, "dossier_id": "gryphe/mythomax/mythomax-l2-13b", "name": "MythoMax L2 13B", "maker": "gryphe", "family": "gryphe/mythomax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 45, "axes_scored": 5, "cautious_projection": 46, "high_projection": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 94, "dossier_id": "nex-agi/nex/nex-n2-5-mini", "name": "Nex N2.5 Mini", "maker": "nex-agi", "family": "nex-agi/nex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 45, "axes_scored": 5, "cautious_projection": 47, "high_projection": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 95, "dossier_id": "nex-agi/nex/nex-n2-5-pro", "name": "Nex N2.5 Pro", "maker": "nex-agi", "family": "nex-agi/nex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 45, "axes_scored": 5, "cautious_projection": 47, "high_projection": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 96, "dossier_id": "sao10k/lunaris/l3-lunaris-8b", "name": "L3 Lunaris 8B", "maker": "sao10k", "family": "sao10k/lunaris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 45, "axes_scored": 5, "cautious_projection": 46, "high_projection": 52, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 97, "dossier_id": "openai/gpt/gpt-5-nano", "name": "GPT-5 nano", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 0.05, "completion_price_per_m": 0.4, "status": "active", "weights": "closed", "empirical_score": 44, "axes_scored": 4, "cautious_projection": 47, "high_projection": 58, "lmsys_elo": 1205, "coding_elo": 1205, "swe_bench": 16.0, "humaneval": 70.8, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Fastest, most cost-efficient version of GPT-5", "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.", "benchmarks": { "aa_intelligence_index": 13.0, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 963, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 98, "dossier_id": "cohere/command/command-a-plus-05-2026", "name": "Command A+", "maker": "cohere", "family": "cohere/command", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": 44, "axes_scored": 3, "cautious_projection": 47, "high_projection": 63, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 99, "dossier_id": "dots-studio/dots/dots-3-note-preview", "name": "Dots 3 Note Preview", "maker": "dots-studio", "family": "dots-studio/dots", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 44, "axes_scored": 6, "cautious_projection": 44, "high_projection": 44, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 100, "dossier_id": "meta/muse/muse-spark", "name": "Muse Spark", "maker": "meta", "family": "meta/muse", "context_length": 1048576, "prompt_price_per_m": 1.25, "completion_price_per_m": 4.25, "status": "active", "weights": "closed", "empirical_score": 42, "axes_scored": 5, "cautious_projection": 44, "high_projection": 49, "lmsys_elo": 1263, "coding_elo": 1263, "swe_bench": 25.6, "humaneval": 78.5, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 30, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 48.1, "aa_coding_index": 75.8, "aa_agentic_index": 55.5, "design_arena_elo": 1189, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 101, "dossier_id": "meta/muse/muse-spark", "name": "Muse Spark", "maker": "meta", "family": "meta/muse", "context_length": 1048576, "prompt_price_per_m": 1.25, "completion_price_per_m": 4.25, "status": "active", "weights": "closed", "empirical_score": 42, "axes_scored": 5, "cautious_projection": 44, "high_projection": 49, "lmsys_elo": 1263, "coding_elo": 1263, "swe_bench": 25.6, "humaneval": 78.5, "twelve_cs_score": 88.1, "speed_tps": 120.0, "cosmos_tests": 25, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 39.6, "aa_coding_index": 72.2, "aa_agentic_index": 43.2, "design_arena_elo": 1142, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 102, "dossier_id": "meta/muse/muse-spark", "name": "Muse Spark", "maker": "meta", "family": "meta/muse", "context_length": 1048576, "prompt_price_per_m": 1.25, "completion_price_per_m": 4.25, "status": "active", "weights": "closed", "empirical_score": 42, "axes_scored": 5, "cautious_projection": 44, "high_projection": 49, "lmsys_elo": 1263, "coding_elo": 1263, "swe_bench": 25.6, "humaneval": 78.5, "twelve_cs_score": 70.2, "speed_tps": 120.0, "cosmos_tests": 26, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 33.7, "aa_coding_index": 71.3, "aa_agentic_index": 25.8, "design_arena_elo": 1147, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 103, "dossier_id": "deepseek/v4/v4-flash", "name": "DeepSeek-V4-Flash", "maker": "deepseek", "family": "deepseek/v4", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": 42, "axes_scored": 6, "cautious_projection": 42, "high_projection": 42, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.5, "humaneval": 70.4, "twelve_cs_score": 71.0, "speed_tps": 240.0, "cosmos_tests": 30, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 24.4, "aa_coding_index": 52.0, "aa_agentic_index": 26.3, "design_arena_elo": 1186, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 104, "dossier_id": "alibaba/qwen/qwen3-7-flash", "name": "Qwen3.7-Flash", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 42, "axes_scored": 5, "cautious_projection": 44, "high_projection": 49, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.3, "humaneval": 70.3, "twelve_cs_score": 59.4, "speed_tps": 482.0, "cosmos_tests": 45, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 105, "dossier_id": "google/gemma/gemma-4-26b-a4b", "name": "Gemma 4 26B A4B", "maker": "google", "family": "google/gemma", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 42, "axes_scored": 5, "cautious_projection": 44, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 106, "dossier_id": "liquid-ai/lfm2-5/lfm2-5-2-6b", "name": "LFM2.5-2.6B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 42, "axes_scored": 5, "cautious_projection": 44, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 107, "dossier_id": "mistral/mistral/mistral-nemo-12b-24-07", "name": "Mistral Nemo 12B", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.15, "status": "retired", "weights": "open-weights", "empirical_score": 42, "axes_scored": 6, "cautious_projection": 42, "high_projection": 42, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our best multilingual open source model released July 2024.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our best multilingual open source model released July 2024.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 108, "dossier_id": "reka/edge/reka-edge-2603", "name": "Reka Edge 2603", "maker": "reka", "family": "reka/edge", "context_length": 16384, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "open-weights", "empirical_score": 41, "axes_scored": 5, "cautious_projection": 43, "high_projection": 49, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.2, "humaneval": 70.2, "twelve_cs_score": 36.1, "speed_tps": 120.0, "cosmos_tests": 9, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 109, "dossier_id": "nvidia/nemotron/nemotron-3-ultra", "name": "Nemotron 3 Ultra", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 1000000, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.5, "status": "active", "weights": "open-weights", "empirical_score": 41, "axes_scored": 6, "cautious_projection": 41, "high_projection": 41, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "NVIDIA positions Ultra for the hardest agent calls: multi-step planning, tool use, code, deep research, and high-stakes retrieval.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 110, "dossier_id": "alibaba/qwen/qwen3-5-9b", "name": "Qwen3.5-9B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 41, "axes_scored": 5, "cautious_projection": 43, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Qwen3.5 multimodal model. Weights repo created 2026-02-27; GitHub news says available 2026-03-02.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 111, "dossier_id": "meta/llama/llama-4-maverick", "name": "Llama 4 Maverick", "maker": "meta", "family": "meta/llama", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 40, "axes_scored": 3, "cautious_projection": 44, "high_projection": 60, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.6, "humaneval": 71.3, "twelve_cs_score": 86.9, "speed_tps": 120.0, "cosmos_tests": 45, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 9.3, "aa_coding_index": 16.3, "aa_agentic_index": 0.6, "design_arena_elo": 898, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 112, "dossier_id": "microsoft/phi/phi-4", "name": "Phi-4", "maker": "microsoft", "family": "microsoft/phi", "context_length": 16384, "prompt_price_per_m": 0.000125, "completion_price_per_m": 0.0005, "status": "active", "weights": "open-weights", "empirical_score": 40, "axes_scored": 6, "cautious_projection": 40, "high_projection": 40, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.3, "humaneval": 70.3, "twelve_cs_score": 83.6, "speed_tps": 120.0, "cosmos_tests": 18, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 5.9, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 113, "dossier_id": "alibaba/qwen/qwen2-5-7b", "name": "Qwen2.5-7B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 40, "axes_scored": 3, "cautious_projection": 44, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B Qwen2.5 instruct model, the most-downloaded Qwen2.5 checkpoint on the retrieved Hugging Face list.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 114, "dossier_id": "alibaba/qwen/qwen3-8b", "name": "Qwen3-8B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 40, "axes_scored": 3, "cautious_projection": 44, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned Qwen3 8B from the April 2025 release. Hybrid thinking can be switched per request on Qwen3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 115, "dossier_id": "moonshot/kimi/kimi-k2", "name": "Kimi K2", "maker": "moonshot", "family": "moonshot/kimi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 40, "axes_scored": 3, "cautious_projection": 44, "high_projection": 60, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 116, "dossier_id": "zhipu/glm/glm-4-7-flash", "name": "GLM-4.7-Flash", "maker": "zhipu", "family": "zhipu/glm", "context_length": 200000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 39, "axes_scored": 5, "cautious_projection": 41, "high_projection": 46, "lmsys_elo": 1205, "coding_elo": 1205, "swe_bench": 16.0, "humaneval": 70.8, "twelve_cs_score": 67.9, "speed_tps": 240.0, "cosmos_tests": 9, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 14.9, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1118, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 117, "dossier_id": "google/gemma/gemma-4-31b", "name": "Gemma 4 31B", "maker": "google", "family": "google/gemma", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 39, "axes_scored": 5, "cautious_projection": 41, "high_projection": 46, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 118, "dossier_id": "meta/llama/llama-3-2-3b", "name": "Llama 3.2 3B", "maker": "meta", "family": "meta/llama", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 39, "axes_scored": 5, "cautious_projection": 41, "high_projection": 46, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 119, "dossier_id": "poolside/laguna/laguna-xs-2-1", "name": "Laguna XS 2.1", "maker": "poolside", "family": "poolside/laguna", "context_length": 262144, "prompt_price_per_m": 0.06, "completion_price_per_m": 0.12, "status": "active", "weights": "open-weights", "empirical_score": 38, "axes_scored": 6, "cautious_projection": 38, "high_projection": 38, "lmsys_elo": 1201, "coding_elo": 1201, "swe_bench": 15.3, "humaneval": 70.2, "twelve_cs_score": 85.9, "speed_tps": 120.0, "cosmos_tests": 21, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 120, "dossier_id": "meta/llama/llama-3-2-1b", "name": "Llama 3.2 1B", "maker": "meta", "family": "meta/llama", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 38, "axes_scored": 5, "cautious_projection": 40, "high_projection": 45, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 121, "dossier_id": "meta/llama-guard/llama-guard-4-12b", "name": "Llama Guard 4 12B", "maker": "meta", "family": "meta/llama-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 36, "axes_scored": 4, "cautious_projection": 40, "high_projection": 51, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.4, "twelve_cs_score": 0.0, "speed_tps": 120.0, "cosmos_tests": 15, "is_free": false, "what_it_is_for": "Classify prompts and responses as safe or unsafe against the MLCommons hazard taxonomy, including multi-image prompts for Llama 4.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 122, "dossier_id": "alibaba/qwen/qwen3-8-27b", "name": "Qwen3.8-27B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 35, "axes_scored": 5, "cautious_projection": 38, "high_projection": 43, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-weight 27B Qwen3.8 multimodal model for local and served use. Thinking is on by default.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 123, "dossier_id": "google/gemma/gemma-3-4b", "name": "Gemma 3 4B", "maker": "google", "family": "google/gemma", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 34, "axes_scored": 6, "cautious_projection": 34, "high_projection": 34, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 124, "dossier_id": "inference-net/schematron/schematron-v2-small", "name": "Schematron V2 Small", "maker": "inference-net", "family": "inference-net/schematron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 33, "axes_scored": 3, "cautious_projection": 40, "high_projection": 57, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 125, "dossier_id": "inference-net/schematron/schematron-v2-turbo", "name": "Schematron V2 Turbo", "maker": "inference-net", "family": "inference-net/schematron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 33, "axes_scored": 3, "cautious_projection": 40, "high_projection": 57, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 126, "dossier_id": "mancer/weaver/weaver", "name": "Weaver", "maker": "mancer", "family": "mancer/weaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 33, "axes_scored": 3, "cautious_projection": 40, "high_projection": 57, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 127, "dossier_id": "google/lyria/lyria-3", "name": "Lyria 3", "maker": "google", "family": "google/lyria", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "preview", "weights": "closed", "empirical_score": 32, "axes_scored": 4, "cautious_projection": 38, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Short music clips from text or an image, including vocals and lyrics.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 128, "dossier_id": "stealth/space-bunny/space-bunny-alpha", "name": "Space Bunny Alpha", "maker": "stealth", "family": "stealth/space-bunny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 32, "axes_scored": 4, "cautious_projection": 38, "high_projection": 49, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 129, "dossier_id": "moonshot/kimi/kimi-k3", "name": "Kimi K3", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 1048576, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "active", "weights": "open-weights", "empirical_score": 29, "axes_scored": 3, "cautious_projection": 38, "high_projection": 54, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 75.8, "speed_tps": 120.0, "cosmos_tests": 24, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 43.6, "aa_coding_index": 76.2, "aa_agentic_index": 50.0, "design_arena_elo": 1218, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 130, "dossier_id": "cohere/command/command-r-08-2024", "name": "Command R 08-2024", "maker": "cohere", "family": "cohere/command", "context_length": 128000, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.6, "status": "active", "weights": "open-weights", "empirical_score": 29, "axes_scored": 3, "cautious_projection": 38, "high_projection": 54, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs_score": 86.6, "speed_tps": 120.0, "cosmos_tests": 22, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 131, "dossier_id": "nvidia/nemotron/nemotron-3-5-content-safety", "name": "Nemotron 3.5 Content Safety", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 29, "axes_scored": 5, "cautious_projection": 33, "high_projection": 38, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 132, "dossier_id": "moonshot/kimi/kimi-k2-thinking", "name": "Kimi K2 Thinking", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 262144, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.5, "status": "retired", "weights": "open-weights", "empirical_score": 28, "axes_scored": 4, "cautious_projection": 35, "high_projection": 46, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs_score": 73.9, "speed_tps": 120.0, "cosmos_tests": 26, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 22.0, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1111, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 133, "dossier_id": "reka/flash/reka-flash-3", "name": "Reka Flash 3", "maker": "reka", "family": "reka/flash", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.8, "status": "active", "weights": "open-weights", "empirical_score": 28, "axes_scored": 6, "cautious_projection": 28, "high_projection": 28, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.5, "humaneval": 70.4, "twelve_cs_score": 69.3, "speed_tps": 240.0, "cosmos_tests": 22, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 6.4, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 134, "dossier_id": "google/lyria/lyria-3-pro", "name": "Lyria 3 Pro", "maker": "google", "family": "google/lyria", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "preview", "weights": "closed", "empirical_score": 27, "axes_scored": 4, "cautious_projection": 34, "high_projection": 45, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full-song generation with vocals, structure, and optional image conditioning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 135, "dossier_id": "cohere/command/command-r-plus-08-2024", "name": "Command R+ 08-2024", "maker": "cohere", "family": "cohere/command", "context_length": 128000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "active", "weights": "open-weights", "empirical_score": 24, "axes_scored": 3, "cautious_projection": 35, "high_projection": 51, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 30, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 136, "dossier_id": "moonshot/kimi/kimi-k2-6", "name": "Kimi K2.6", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 262144, "prompt_price_per_m": 0.95, "completion_price_per_m": 4.0, "status": "active", "weights": "open-weights", "empirical_score": 24, "axes_scored": 3, "cautious_projection": 35, "high_projection": 51, "lmsys_elo": 1251, "coding_elo": 1251, "swe_bench": 23.5, "humaneval": 76.8, "twelve_cs_score": 30.4, "speed_tps": 120.0, "cosmos_tests": 55, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 27.0, "aa_coding_index": 61.8, "aa_agentic_index": 20.5, "design_arena_elo": 1106, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 137, "dossier_id": "thinking-machines/inkling/inkling", "name": "Inkling", "maker": "thinking-machines", "family": "thinking-machines/inkling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 24, "axes_scored": 5, "cautious_projection": 28, "high_projection": 33, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 138, "dossier_id": "thinking-machines/inkling/inkling-small", "name": "Inkling Small", "maker": "thinking-machines", "family": "thinking-machines/inkling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": 24, "axes_scored": 5, "cautious_projection": 28, "high_projection": 33, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": null, "summary_intro": null, "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 139, "dossier_id": "mistral/mistral/mistral-saba-25-02", "name": "Mistral Saba", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": 18, "axes_scored": 6, "cautious_projection": 18, "high_projection": 18, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A finetuned Small model trained on meticulously curated datasets from across the Middle East and South Asia.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 140, "dossier_id": "meta/llama/llama-4-scout", "name": "Llama 4 Scout", "maker": "meta", "family": "meta/llama", "context_length": 10000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": 4, "axes_scored": 5, "cautious_projection": 11, "high_projection": 16, "lmsys_elo": 1208, "coding_elo": 1208, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs_score": 9.7, "speed_tps": 120.0, "cosmos_tests": 55, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 6.5, "aa_coding_index": 8.2, "aa_agentic_index": 0.5, "design_arena_elo": 788, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 141, "dossier_id": "openai/o/o1", "name": "o1", "maker": "openai", "family": "openai/o", "context_length": 200000, "prompt_price_per_m": 15.0, "completion_price_per_m": 60.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1365, "coding_elo": 1375, "swe_bench": 48.9, "humaneval": 92.4, "twelve_cs_score": 99.5, "speed_tps": 42.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Previous full o-series reasoning model", "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.", "benchmarks": { "aa_intelligence_index": 92.0, "aa_coding_index": 94.0, "aa_agentic_index": 85.0, "design_arena_elo": null, "math_500": 96.4, "gpqa_diamond": 77.3, "ifeval": 89.0, "bfcl_tool_calling": 88.0 } }, { "rank": 142, "dossier_id": "openai/gpt/gpt-6-astra", "name": "GPT-6 Astra", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 10.0, "completion_price_per_m": 50.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Our most capable model for the most demanding work.", "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.", "benchmarks": { "aa_intelligence_index": 52.7, "aa_coding_index": 76.9, "aa_agentic_index": 51.0, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 143, "dossier_id": "google/gemini/gemini-4-argon", "name": "Gemini 4 Argon", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 2.0, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 98.0, "speed_tps": 240.0, "cosmos_tests": 0, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 52.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1438, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 144, "dossier_id": "openai/gpt/gpt-6-sol", "name": "GPT-6 Sol", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 2.0, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Built to power complex coding and agentic workflows.", "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.", "benchmarks": { "aa_intelligence_index": 47.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 145, "dossier_id": "openai/gpt/gpt-daybreak-blue-latest", "name": "Daybreak Blue", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 18, "is_free": false, "what_it_is_for": "An alias for flagship general-purpose models with safeguards for defensive cybersecurity work.", "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.", "benchmarks": { "aa_intelligence_index": 47.0, "aa_coding_index": 77.4, "aa_agentic_index": 50.2, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 146, "dossier_id": "openai/gpt/gpt-5", "name": "GPT-5", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 14, "is_free": false, "what_it_is_for": "Previous intelligent reasoning model for coding and agentic tasks with configurable reasoning effort", "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.", "benchmarks": { "aa_intelligence_index": 23.0, "aa_coding_index": 37.8, "aa_agentic_index": null, "design_arena_elo": 1053, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 147, "dossier_id": "openai/o/o3-pro", "name": "o3-pro", "maker": "openai", "family": "openai/o", "context_length": 200000, "prompt_price_per_m": 20.0, "completion_price_per_m": 80.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Version of o3 with more compute for better responses", "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.", "benchmarks": { "aa_intelligence_index": 21.9, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 148, "dossier_id": "openai/o/o1-pro", "name": "o1-pro", "maker": "openai", "family": "openai/o", "context_length": 200000, "prompt_price_per_m": 150.0, "completion_price_per_m": 600.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Version of o1 with more compute for better responses", "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.", "benchmarks": { "aa_intelligence_index": 12.4, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 149, "dossier_id": "perplexity/sonar/sonar-pro", "name": "Sonar Pro", "maker": "perplexity", "family": "perplexity/sonar", "context_length": 200000, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "summary_intro": "Perplexity model `sonar-pro`. Status: deprecated. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": 7.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 150, "dossier_id": "openai/gpt/gpt-4-turbo", "name": "GPT-4 Turbo", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 10.0, "completion_price_per_m": 30.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 52.0, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "An older high-intelligence GPT model", "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.", "benchmarks": { "aa_intelligence_index": 7.0, "aa_coding_index": 21.5, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 151, "dossier_id": "openai/gpt/gpt-4", "name": "GPT-4", "maker": "openai", "family": "openai/gpt", "context_length": 8192, "prompt_price_per_m": 30.0, "completion_price_per_m": 60.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 52.0, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "An older high-intelligence GPT model", "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.", "benchmarks": { "aa_intelligence_index": 6.7, "aa_coding_index": 13.1, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 152, "dossier_id": "sakana/fugu/fugu-ultra", "name": "Fugu Ultra", "maker": "sakana", "family": "sakana/fugu", "context_length": 1000000, "prompt_price_per_m": 5.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 153, "dossier_id": "meta/muse/muse-wonder", "name": "Muse Wonder", "maker": "meta", "family": "meta/muse", "context_length": 1048576, "prompt_price_per_m": 4.0, "completion_price_per_m": 20.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 98.0, "speed_tps": 120.0, "cosmos_tests": 0, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1232, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 154, "dossier_id": "openai/gpt/gpt-5-pro", "name": "GPT-5 Pro", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 15.0, "completion_price_per_m": 120.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 50.0, "humaneval": 95.0, "twelve_cs_score": 52.0, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Version of GPT-5 that produces smarter and more precise responses", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 155, "dossier_id": "google/gemini-image/gemini-3-pro-image", "name": "Nano Banana Pro", "maker": "google", "family": "google/gemini-image", "context_length": 65536, "prompt_price_per_m": 2.0, "completion_price_per_m": 12.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 45.0, "humaneval": 94.0, "twelve_cs_score": 28.2, "speed_tps": 240.0, "cosmos_tests": 12, "is_free": false, "what_it_is_for": "Google describes it as the professional image model for design, product mockups, accurate text, and search-grounded visuals.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 156, "dossier_id": "moonshot/kimi/kimi-latest", "name": "Kimi Latest", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 23.5, "speed_tps": 120.0, "cosmos_tests": 15, "is_free": false, "what_it_is_for": "Let API users track the assistant. Moonshot told users who needed stable prompts to stay on moonshot-v1 instead.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 157, "dossier_id": "openai/audio/gpt-audio", "name": "GPT-Audio", "maker": "openai", "family": "openai/audio", "context_length": 128000, "prompt_price_per_m": 32.0, "completion_price_per_m": 64.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1350, "coding_elo": 1350, "swe_bench": 40.0, "humaneval": 90.0, "twelve_cs_score": 23.5, "speed_tps": 120.0, "cosmos_tests": 15, "is_free": false, "what_it_is_for": "For audio inputs and outputs with Chat Completions API", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 158, "dossier_id": "anthropic/sonnet/claude-3-5-sonnet", "name": "Claude Sonnet 3.5", "maker": "anthropic", "family": "anthropic/sonnet", "context_length": 200000, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1335, "coding_elo": 1335, "swe_bench": 49.0, "humaneval": 93.7, "twelve_cs_score": null, "speed_tps": 82.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 72.4, "aa_coding_index": 85.2, "aa_agentic_index": 68.9, "design_arena_elo": 1335, "math_500": 78.0, "gpqa_diamond": 65.0, "ifeval": 88.0, "bfcl_tool_calling": 89.5 } }, { "rank": 159, "dossier_id": "openai/gpt/gpt-4o", "name": "GPT-4o", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1332, "coding_elo": 1330, "swe_bench": 38.8, "humaneval": 90.2, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Fast, intelligent, flexible GPT model", "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.", "benchmarks": { "aa_intelligence_index": 68.0, "aa_coding_index": 78.0, "aa_agentic_index": 64.0, "design_arena_elo": 866, "math_500": 76.6, "gpqa_diamond": 53.6, "ifeval": 84.3, "bfcl_tool_calling": 88.2 } }, { "rank": 160, "dossier_id": "xiaomi/mimo-v2-6/mimo-v2-6-pro-ultraspeed", "name": "MiMo-V2.6-Pro-UltraSpeed", "maker": "xiaomi", "family": "xiaomi/mimo-v2-6", "context_length": 1048576, "prompt_price_per_m": 4.35, "completion_price_per_m": 8.7, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1330, "coding_elo": 1330, "swe_bench": 36.8, "humaneval": 87.4, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Xiaomi aims it at real-time and latency-sensitive workloads that still want Pro quality.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 161, "dossier_id": "openai/o/o1-mini", "name": "o1-mini", "maker": "openai", "family": "openai/o", "context_length": 128000, "prompt_price_per_m": 3.0, "completion_price_per_m": 12.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1315, "coding_elo": 1320, "swe_bench": 41.0, "humaneval": 90.0, "twelve_cs_score": null, "speed_tps": 75.0, "cosmos_tests": 12, "is_free": false, "what_it_is_for": "A small model alternative to o1", "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.", "benchmarks": { "aa_intelligence_index": 76.0, "aa_coding_index": 82.0, "aa_agentic_index": 68.0, "design_arena_elo": 1320, "math_500": 90.0, "gpqa_diamond": 60.0, "ifeval": 84.0, "bfcl_tool_calling": 85.0 } }, { "rank": 162, "dossier_id": "openai/o/o3", "name": "o3", "maker": "openai", "family": "openai/o", "context_length": 200000, "prompt_price_per_m": 1.0, "completion_price_per_m": 4.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1320, "coding_elo": 1320, "swe_bench": 35.0, "humaneval": 86.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Reasoning model for complex tasks, succeeded by GPT-5", "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.", "benchmarks": { "aa_intelligence_index": 20.2, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1018, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 163, "dossier_id": "perplexity/sonar/sonar-reasoning-pro", "name": "Sonar Reasoning Pro", "maker": "perplexity", "family": "perplexity/sonar", "context_length": 128000, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1320, "coding_elo": 1320, "swe_bench": 35.0, "humaneval": 86.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "summary_intro": "Perplexity model `sonar-reasoning-pro`. Status: deprecated. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": 11.8, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 164, "dossier_id": "perplexity/sonar/sonar-deep-research", "name": "Sonar Deep Research", "maker": "perplexity", "family": "perplexity/sonar", "context_length": 128000, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1320, "coding_elo": 1320, "swe_bench": 35.0, "humaneval": 86.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "summary_intro": "Perplexity model `sonar-deep-research`. Status: deprecated. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 165, "dossier_id": "mistral/mistral/mistral-medium-3-5-26-04", "name": "Mistral Medium 3.5", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 1.5, "completion_price_per_m": 7.5, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1312, "coding_elo": 1312, "swe_bench": 33.8, "humaneval": 85.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 14.2, "aa_coding_index": 46.9, "aa_agentic_index": 7.0, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 166, "dossier_id": "zhipu/glm/glm-5-2", "name": "GLM-5.2", "maker": "zhipu", "family": "zhipu/glm", "context_length": 1000000, "prompt_price_per_m": 1.4, "completion_price_per_m": 4.4, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1305, "coding_elo": 1305, "swe_bench": 32.5, "humaneval": 84.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 22, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 33.7, "aa_coding_index": 68.8, "aa_agentic_index": 38.4, "design_arena_elo": 1171, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 167, "dossier_id": "xai/grok/grok-2", "name": "Grok-2", "maker": "xai", "family": "xai/grok", "context_length": 131072, "prompt_price_per_m": 2.0, "completion_price_per_m": 10.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1305, "coding_elo": 1300, "swe_bench": 37.0, "humaneval": 88.5, "twelve_cs_score": null, "speed_tps": 85.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 64.0, "aa_coding_index": 74.0, "aa_agentic_index": 62.0, "design_arena_elo": 1300, "math_500": 76.0, "gpqa_diamond": 52.0, "ifeval": 84.0, "bfcl_tool_calling": 86.0 } }, { "rank": 168, "dossier_id": "mistral/mistral/mistral-large-2-1-24-11", "name": "Mistral Large 2.1", "maker": "mistral", "family": "mistral/mistral", "context_length": 128000, "prompt_price_per_m": 2.0, "completion_price_per_m": 6.0, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1300, "coding_elo": 1295, "swe_bench": 38.0, "humaneval": 88.0, "twelve_cs_score": null, "speed_tps": 75.0, "cosmos_tests": 12, "is_free": false, "what_it_is_for": "Our top-tier large model for high-complexity tasks with the lastest version released November 2024.", "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.", "benchmarks": { "aa_intelligence_index": 62.0, "aa_coding_index": 73.0, "aa_agentic_index": 59.0, "design_arena_elo": 1295, "math_500": 74.0, "gpqa_diamond": 50.0, "ifeval": 84.0, "bfcl_tool_calling": 85.0 } }, { "rank": 169, "dossier_id": "anthropic/haiku/claude-3-5-haiku", "name": "Claude Haiku 3.5", "maker": "anthropic", "family": "anthropic/haiku", "context_length": 200000, "prompt_price_per_m": 0.8, "completion_price_per_m": 4.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1290, "coding_elo": 1295, "swe_bench": 40.6, "humaneval": 88.9, "twelve_cs_score": null, "speed_tps": 115.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 52.0, "aa_coding_index": 68.0, "aa_agentic_index": 54.0, "design_arena_elo": 1295, "math_500": 69.4, "gpqa_diamond": 51.2, "ifeval": 84.5, "bfcl_tool_calling": 84.0 } }, { "rank": 170, "dossier_id": "mistral/codestral/codestral-25-01", "name": "Codestral", "maker": "mistral", "family": "mistral/codestral", "context_length": 256000, "prompt_price_per_m": 0.3, "completion_price_per_m": 0.9, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1280, "coding_elo": 1290, "swe_bench": 29.0, "humaneval": 89.0, "twelve_cs_score": null, "speed_tps": 105.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 52.0, "aa_coding_index": 71.0, "aa_agentic_index": 52.0, "design_arena_elo": 1290, "math_500": 70.0, "gpqa_diamond": 45.0, "ifeval": 80.0, "bfcl_tool_calling": 83.0 } }, { "rank": 171, "dossier_id": "sakana/fugu/fugu-max", "name": "Fugu Max", "maker": "sakana", "family": "sakana/fugu", "context_length": 1000000, "prompt_price_per_m": 2.0, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1290, "coding_elo": 1290, "swe_bench": 30.0, "humaneval": 82.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 172, "dossier_id": "writer/palmyra-x/palmyra-x5", "name": "Palmyra X5", "maker": "writer", "family": "writer/palmyra-x", "context_length": 1000000, "prompt_price_per_m": 0.6, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1290, "coding_elo": 1290, "swe_bench": 30.0, "humaneval": 82.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 173, "dossier_id": "zhipu/glm/glm-5-3", "name": "GLM-5.3", "maker": "zhipu", "family": "zhipu/glm", "context_length": 1000000, "prompt_price_per_m": 1.4, "completion_price_per_m": 4.4, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1272, "coding_elo": 1272, "swe_bench": 27.0, "humaneval": 79.6, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 18, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 44.8, "aa_coding_index": 74.8, "aa_agentic_index": 53.1, "design_arena_elo": 1329, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 174, "dossier_id": "openai/gpt/gpt-4o-mini", "name": "GPT-4o Mini", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1275, "coding_elo": 1270, "swe_bench": 16.5, "humaneval": 87.2, "twelve_cs_score": 99.5, "speed_tps": 145.0, "cosmos_tests": 38, "is_free": false, "what_it_is_for": "Fast, affordable small model for focused tasks", "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.", "benchmarks": { "aa_intelligence_index": 44.0, "aa_coding_index": 56.0, "aa_agentic_index": 48.0, "design_arena_elo": null, "math_500": 70.2, "gpqa_diamond": 40.2, "ifeval": 80.0, "bfcl_tool_calling": 84.0 } }, { "rank": 175, "dossier_id": "alibaba/qwen/qwen3-7-max", "name": "Qwen3.7-Max", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1266, "coding_elo": 1266, "swe_bench": 26.1, "humaneval": 78.8, "twelve_cs_score": 83.6, "speed_tps": 482.0, "cosmos_tests": 18, "is_free": false, "what_it_is_for": "Largest Qwen3.7 model, aimed at agent work: coding, office tasks, and long-running execution.", "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.", "benchmarks": { "aa_intelligence_index": 29.5, "aa_coding_index": 66.0, "aa_agentic_index": 22.5, "design_arena_elo": 1125, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 176, "dossier_id": "openai/o/o4-mini", "name": "o4-mini", "maker": "openai", "family": "openai/o", "context_length": 200000, "prompt_price_per_m": 1.1, "completion_price_per_m": 4.4, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1266, "coding_elo": 1266, "swe_bench": 26.0, "humaneval": 78.8, "twelve_cs_score": 99.5, "speed_tps": 240.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Fast, cost-efficient reasoning model, succeeded by GPT-5 Mini", "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.", "benchmarks": { "aa_intelligence_index": 16.7, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 851, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 177, "dossier_id": "openai/o/o3-mini", "name": "o3-mini", "maker": "openai", "family": "openai/o", "context_length": 200000, "prompt_price_per_m": 1.1, "completion_price_per_m": 4.4, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1266, "coding_elo": 1266, "swe_bench": 26.0, "humaneval": 78.8, "twelve_cs_score": 99.5, "speed_tps": 240.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "A small model alternative to o3", "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.", "benchmarks": { "aa_intelligence_index": 12.5, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 178, "dossier_id": "anthropic/opus/claude-3-opus", "name": "Claude Opus 3", "maker": "anthropic", "family": "anthropic/opus", "context_length": 200000, "prompt_price_per_m": 15.0, "completion_price_per_m": 75.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1270, "coding_elo": 1260, "swe_bench": 32.0, "humaneval": 84.9, "twelve_cs_score": null, "speed_tps": 35.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 48.0, "aa_coding_index": 62.0, "aa_agentic_index": 50.0, "design_arena_elo": 1260, "math_500": 60.1, "gpqa_diamond": 50.4, "ifeval": 84.0, "bfcl_tool_calling": 81.0 } }, { "rank": 179, "dossier_id": "zhipu/glm/glm-5-turbo", "name": "GLM-5-Turbo", "maker": "zhipu", "family": "zhipu/glm", "context_length": 200000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1260, "coding_elo": 1260, "swe_bench": 25.0, "humaneval": 78.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 14, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 26.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1245, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 180, "dossier_id": "zhipu/glm-v/glm-5v-turbo", "name": "GLM-5V-Turbo", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 200000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1260, "coding_elo": 1260, "swe_bench": 25.0, "humaneval": 78.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 23.5, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1067, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 181, "dossier_id": "mistral/mistral/mistral-large-2-0-24-07", "name": "Mistral Large 2.0", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1265, "coding_elo": 1260, "swe_bench": 27.5, "humaneval": 84.0, "twelve_cs_score": 52.0, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "A powerfull model with open weights under a MRL license, competing agains SOTA much larger models.", "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.", "benchmarks": { "aa_intelligence_index": 7.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 182, "dossier_id": "sakana/namazu/sakana-namazu", "name": "Sakana Namazu", "maker": "sakana", "family": "sakana/namazu", "context_length": null, "prompt_price_per_m": 0.95, "completion_price_per_m": 4.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1260, "coding_elo": 1260, "swe_bench": 25.0, "humaneval": 78.0, "twelve_cs_score": 36.1, "speed_tps": 120.0, "cosmos_tests": 9, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 183, "dossier_id": "alibaba/qwen/qwen3-5-397b-a17b", "name": "Qwen3.5-397B-A17B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1252, "coding_elo": 1252, "swe_bench": 23.8, "humaneval": 77.0, "twelve_cs_score": 67.9, "speed_tps": 482.0, "cosmos_tests": 9, "is_free": false, "what_it_is_for": "First Qwen3.5 open model, a native multimodal agent model with 201 languages and dialects.", "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.", "benchmarks": { "aa_intelligence_index": 21.4, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1158, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 184, "dossier_id": "cohere/command/command-r-plus-04-2024", "name": "Command R+ 04-2024", "maker": "cohere", "family": "cohere/command", "context_length": 128000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1260, "coding_elo": 1250, "swe_bench": 22.0, "humaneval": 78.0, "twelve_cs_score": null, "speed_tps": 65.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 52.0, "aa_coding_index": 58.0, "aa_agentic_index": 62.0, "design_arena_elo": 1250, "math_500": 55.0, "gpqa_diamond": 38.0, "ifeval": 82.0, "bfcl_tool_calling": 86.0 } }, { "rank": 185, "dossier_id": "moonshot/kimi/kimi-k2-7-code", "name": "Kimi K2.7 Code", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 262144, "prompt_price_per_m": 0.95, "completion_price_per_m": 4.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1250, "coding_elo": 1250, "swe_bench": 23.4, "humaneval": 76.7, "twelve_cs_score": 23.5, "speed_tps": 120.0, "cosmos_tests": 15, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 25.8, "aa_coding_index": 60.8, "aa_agentic_index": 21.0, "design_arena_elo": 1095, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 186, "dossier_id": "zhipu/glm/glm-5-1", "name": "GLM-5.1", "maker": "zhipu", "family": "zhipu/glm", "context_length": 200000, "prompt_price_per_m": 1.4, "completion_price_per_m": 4.4, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1245, "coding_elo": 1245, "swe_bench": 22.6, "humaneval": 76.1, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 14, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 26.1, "aa_coding_index": 55.8, "aa_agentic_index": 23.9, "design_arena_elo": 1336, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 187, "dossier_id": "nous-research/hermes/hermes-4-405b", "name": "Hermes 4 405B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": 131072, "prompt_price_per_m": 1.0, "completion_price_per_m": 3.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1245, "coding_elo": 1245, "swe_bench": 22.5, "humaneval": 76.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 7.5, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 188, "dossier_id": "mistral/ministral/ministral-8b-24-1", "name": "Ministral 8B", "maker": "mistral", "family": "mistral/ministral", "context_length": 128000, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1245, "coding_elo": 1240, "swe_bench": 18.0, "humaneval": 79.0, "twelve_cs_score": null, "speed_tps": 150.0, "cosmos_tests": 12, "is_free": false, "what_it_is_for": "Powerful edge model with extremely high performance/price ratio. Learn more in our [blog post](https://mistral.ai/news/ministraux/)", "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/)", "benchmarks": { "aa_intelligence_index": 44.0, "aa_coding_index": 54.0, "aa_agentic_index": 48.0, "design_arena_elo": 1240, "math_500": 58.0, "gpqa_diamond": 38.0, "ifeval": 79.0, "bfcl_tool_calling": 81.0 } }, { "rank": 189, "dossier_id": "stepfun/step/step-5-preview", "name": "Step 5 Preview", "maker": "stepfun", "family": "stepfun/step", "context_length": 1000000, "prompt_price_per_m": 7.0, "completion_price_per_m": 20.0, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1240, "coding_elo": 1240, "swe_bench": 21.8, "humaneval": 75.4, "twelve_cs_score": 98.0, "speed_tps": 120.0, "cosmos_tests": 0, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 43.7, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 190, "dossier_id": "alibaba/qwen/qwen3-5-27b", "name": "Qwen3.5-27B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1239, "coding_elo": 1239, "swe_bench": 21.5, "humaneval": 75.2, "twelve_cs_score": 52.0, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Dense 27B Qwen3.5 open model, released with the mid-size set.", "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.", "benchmarks": { "aa_intelligence_index": 22.9, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 191, "dossier_id": "xai/grok/grok-4-20-0309-reasoning", "name": "Grok 4.20 Reasoning", "maker": "xai", "family": "xai/grok", "context_length": 1000000, "prompt_price_per_m": 1.25, "completion_price_per_m": 2.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 25.7, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1047, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 192, "dossier_id": "moonshot/kimi/kimi-k2-5", "name": "Kimi K2.5", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 262144, "prompt_price_per_m": 4.0, "completion_price_per_m": 21.0, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 23.5, "aa_coding_index": 46.8, "aa_agentic_index": null, "design_arena_elo": 1120, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 193, "dossier_id": "bytedance/seed/seed-2-1-turbo", "name": "Seed 2.1 Turbo", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1237, "coding_elo": 1237, "swe_bench": 21.2, "humaneval": 75.0, "twelve_cs_score": 26.3, "speed_tps": 120.0, "cosmos_tests": 48, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 194, "dossier_id": "openai/audio/gpt-audio-mini", "name": "GPT-Audio Mini", "maker": "openai", "family": "openai/audio", "context_length": 128000, "prompt_price_per_m": 10.0, "completion_price_per_m": 20.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1236, "coding_elo": 1236, "swe_bench": 21.0, "humaneval": 74.8, "twelve_cs_score": 23.5, "speed_tps": 240.0, "cosmos_tests": 15, "is_free": false, "what_it_is_for": "A cost-efficient version of GPT Audio", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 195, "dossier_id": "zhipu/glm/glm-4-7", "name": "GLM-4.7", "maker": "zhipu", "family": "zhipu/glm", "context_length": 200000, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1233, "coding_elo": 1233, "swe_bench": 20.5, "humaneval": 74.4, "twelve_cs_score": 90.0, "speed_tps": 120.0, "cosmos_tests": 30, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 22.2, "aa_coding_index": 45.3, "aa_agentic_index": null, "design_arena_elo": 1114, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 196, "dossier_id": "zhipu/glm/glm-4-5", "name": "GLM-4.5", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.2, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1233, "coding_elo": 1233, "swe_bench": 20.5, "humaneval": 74.4, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 12.8, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1167, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 197, "dossier_id": "mistral/mistral/mistral-large-4-0", "name": "Mistral Large 4", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.68, "completion_price_per_m": 2.09, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1231, "coding_elo": 1231, "swe_bench": 20.2, "humaneval": 74.2, "twelve_cs_score": 98.0, "speed_tps": 120.0, "cosmos_tests": 0, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 38.4, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1248, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 198, "dossier_id": "alibaba/qwen/qwen3-5-122b-a10b", "name": "Qwen3.5-122B-A10B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1231, "coding_elo": 1231, "swe_bench": 20.2, "humaneval": 74.2, "twelve_cs_score": 99.5, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Mid-size sparse Qwen3.5 open model, announced with the 35B-A3B and 27B models.", "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.", "benchmarks": { "aa_intelligence_index": 17.7, "aa_coding_index": 43.3, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 199, "dossier_id": "openai/gpt/gpt-5-mini", "name": "GPT-5 Mini", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 0.25, "completion_price_per_m": 2.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs_score": 99.5, "speed_tps": 240.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Strong intelligence for cost sensitive, low latency, high volume workloads", "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.", "benchmarks": { "aa_intelligence_index": 20.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1034, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 200, "dossier_id": "zhipu/glm/glm-4-6", "name": "GLM-4.6", "maker": "zhipu", "family": "zhipu/glm", "context_length": 200000, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs_score": 67.9, "speed_tps": 120.0, "cosmos_tests": 9, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 18.5, "aa_coding_index": 45.8, "aa_agentic_index": null, "design_arena_elo": 1086, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 201, "dossier_id": "mistral/mistral/mistral-medium-3-5-26-04", "name": "Mistral Medium 3.5", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 1.5, "completion_price_per_m": 7.5, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 9.0, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1081, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 202, "dossier_id": "mistral/devstral/devstral-2-25-12", "name": "Devstral 2", "maker": "mistral", "family": "mistral/devstral", "context_length": null, "prompt_price_per_m": 0.4, "completion_price_per_m": 2.0, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 8.6, "aa_coding_index": 31.3, "aa_agentic_index": 2.3, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 203, "dossier_id": "openai/gpt/gpt-3-5-turbo", "name": "GPT-3.5 Turbo", "maker": "openai", "family": "openai/gpt", "context_length": 16385, "prompt_price_per_m": 0.5, "completion_price_per_m": 1.5, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1230, "coding_elo": 1230, "swe_bench": 20.0, "humaneval": 74.0, "twelve_cs_score": 0.0, "speed_tps": 120.0, "cosmos_tests": 9, "is_free": false, "what_it_is_for": "Legacy GPT model for cheaper chat and non-chat tasks", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 204, "dossier_id": "alibaba/qwen/qwen3-6-plus", "name": "Qwen3.6-Plus", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1229, "coding_elo": 1229, "swe_bench": 19.9, "humaneval": 73.9, "twelve_cs_score": 42.9, "speed_tps": 482.0, "cosmos_tests": 57, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 27.0, "aa_coding_index": 54.5, "aa_agentic_index": null, "design_arena_elo": 1199, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 205, "dossier_id": "zhipu/glm/glm-5", "name": "GLM-5", "maker": "zhipu", "family": "zhipu/glm", "context_length": 200000, "prompt_price_per_m": 1.0, "completion_price_per_m": 3.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1228, "coding_elo": 1228, "swe_bench": 19.8, "humaneval": 73.8, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "summary_intro": "GLM-5 is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": 27.9, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1148, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 206, "dossier_id": "zhipu/glm-v/glm-4-5v", "name": "GLM-4.5V", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 64000, "prompt_price_per_m": 0.6, "completion_price_per_m": 1.8, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1226, "coding_elo": 1226, "swe_bench": 19.5, "humaneval": 73.6, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 7.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 207, "dossier_id": "mistral/mistral/mistral-large-3-25-12", "name": "Mistral Large 3", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.5, "completion_price_per_m": 1.5, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1222, "coding_elo": 1222, "swe_bench": 18.8, "humaneval": 73.0, "twelve_cs_score": 36.1, "speed_tps": 120.0, "cosmos_tests": 9, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 9.3, "aa_coding_index": 20.1, "aa_agentic_index": 1.0, "design_arena_elo": 1096, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 208, "dossier_id": "alibaba/qwen/qwen3-7-plus", "name": "Qwen3.7-Plus", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": 0.276, "completion_price_per_m": 1.101, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1219, "coding_elo": 1219, "swe_bench": 18.2, "humaneval": 72.6, "twelve_cs_score": 29.4, "speed_tps": 482.0, "cosmos_tests": 42, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 25.2, "aa_coding_index": 55.9, "aa_agentic_index": 17.5, "design_arena_elo": 1236, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 209, "dossier_id": "minimax/m/minimax-m3", "name": "MiniMax-M3", "maker": "minimax", "family": "minimax/m", "context_length": 1000000, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1218, "coding_elo": 1218, "swe_bench": 18.0, "humaneval": 72.4, "twelve_cs_score": 27.7, "speed_tps": 240.0, "cosmos_tests": 45, "is_free": false, "what_it_is_for": "The maker positions M3 for agentic reasoning, tool use, coding, multimodal chat, and long-context work.", "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.", "benchmarks": { "aa_intelligence_index": 29.2, "aa_coding_index": 58.6, "aa_agentic_index": 29.5, "design_arena_elo": 1125, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 210, "dossier_id": "minimax/m/minimax-m2", "name": "MiniMax-M2", "maker": "minimax", "family": "minimax/m", "context_length": 204800, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1218, "coding_elo": 1218, "swe_bench": 18.0, "humaneval": 72.4, "twelve_cs_score": 99.5, "speed_tps": 240.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "MiniMax built M2 for coding workflows and multi-step tool use at a small activation size.", "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.", "benchmarks": { "aa_intelligence_index": 18.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1095, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 211, "dossier_id": "alibaba/qwen/qwen3-next-80b-a3b", "name": "Qwen3-Next-80B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": 1.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1218, "coding_elo": 1218, "swe_bench": 18.0, "humaneval": 72.4, "twelve_cs_score": 52.0, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Efficiency-focused Qwen3-Next model. Sold as separate instruct and thinking endpoints.", "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.", "benchmarks": { "aa_intelligence_index": 11.2, "aa_coding_index": 17.4, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 212, "dossier_id": "zhipu/glm/glm-5-3-flashx", "name": "GLM-5.3-FlashX", "maker": "zhipu", "family": "zhipu/glm", "context_length": 1000000, "prompt_price_per_m": 0.37, "completion_price_per_m": 1.25, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1218, "coding_elo": 1218, "swe_bench": 18.1, "humaneval": 72.5, "twelve_cs_score": 33.4, "speed_tps": 240.0, "cosmos_tests": 46, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 213, "dossier_id": "alibaba/qwen/qwen3-next-80b-a3b", "name": "Qwen3-Next-80B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": 1.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1216, "coding_elo": 1216, "swe_bench": 17.8, "humaneval": 72.2, "twelve_cs_score": 99.5, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Efficiency-focused Qwen3-Next model. Sold as separate instruct and thinking endpoints.", "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.", "benchmarks": { "aa_intelligence_index": 9.6, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 214, "dossier_id": "alibaba/qwen/qwen3-6-flash", "name": "Qwen3.6-Flash", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1216, "coding_elo": 1216, "swe_bench": 17.8, "humaneval": 72.2, "twelve_cs_score": 26.3, "speed_tps": 482.0, "cosmos_tests": 48, "is_free": false, "what_it_is_for": "Previous Flash tier. The text index lists snapshot qwen3.6-flash-2026-04-16.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 215, "dossier_id": "alibaba/qwen/qwen3-5-35b-a3b", "name": "Qwen3.5-35B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1215, "coding_elo": 1215, "swe_bench": 17.5, "humaneval": 72.0, "twelve_cs_score": 52.0, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Sparse Qwen3.5 model with about 3B active parameters, released 2026-02-24.", "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.", "benchmarks": { "aa_intelligence_index": 19.3, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 216, "dossier_id": "perplexity/sonar/sonar", "name": "Sonar", "maker": "perplexity", "family": "perplexity/sonar", "context_length": 128000, "prompt_price_per_m": 0.25, "completion_price_per_m": 2.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1215, "coding_elo": 1215, "swe_bench": 17.5, "humaneval": 72.0, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "summary_intro": "Perplexity model `sonar`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": 7.7, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 217, "dossier_id": "alibaba/qwen/qwen3-6-35b-a3b", "name": "Qwen3.6-35B-A3B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1214, "coding_elo": 1214, "swe_bench": 17.4, "humaneval": 71.9, "twelve_cs_score": 63.9, "speed_tps": 482.0, "cosmos_tests": 24, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 18.2, "aa_coding_index": 41.9, "aa_agentic_index": 13.1, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 218, "dossier_id": "alibaba/qwen-coder/qwen3-coder-flash", "name": "Qwen3-Coder-Flash", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": 1000000, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.5, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1214, "coding_elo": 1214, "swe_bench": 17.4, "humaneval": 72.0, "twelve_cs_score": 99.5, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 219, "dossier_id": "xiaomi/mimo-v2-6/mimo-v2-6-pro", "name": "MiMo-V2.6-Pro", "maker": "xiaomi", "family": "xiaomi/mimo-v2-6", "context_length": 1048576, "prompt_price_per_m": 0.435, "completion_price_per_m": 0.87, "status": "active", "weights": "open-weights", "empirical_score": 0, "axes_scored": 3, "cautious_projection": 21, "high_projection": 37, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.7, "twelve_cs_score": 14.3, "speed_tps": 120.0, "cosmos_tests": 58, "is_free": false, "what_it_is_for": "Xiaomi recommends it for complex projects, long-horizon work, cybersecurity, and research, with text, image, video, and audio input.", "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.", "benchmarks": { "aa_intelligence_index": 46.3, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1290, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 220, "dossier_id": "zhipu/glm/glm-5-3-flash", "name": "GLM-5.3-Flash", "maker": "zhipu", "family": "zhipu/glm", "context_length": 1000000, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.5, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.8, "twelve_cs_score": 7.2, "speed_tps": 240.0, "cosmos_tests": 867, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 41.8, "aa_coding_index": 71.5, "aa_agentic_index": 50.9, "design_arena_elo": 1314, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 221, "dossier_id": "zhipu/glm-v/glm-4-6v", "name": "GLM-4.6V", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 128000, "prompt_price_per_m": 0.3, "completion_price_per_m": 0.9, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.8, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 11.2, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 222, "dossier_id": "mistral/codestral/codestral-25-08", "name": "Codestral", "maker": "mistral", "family": "mistral/codestral", "context_length": null, "prompt_price_per_m": 0.3, "completion_price_per_m": 0.9, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1213, "coding_elo": 1213, "swe_bench": 17.2, "humaneval": 71.8, "twelve_cs_score": 87.6, "speed_tps": 120.0, "cosmos_tests": 24, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1005, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 223, "dossier_id": "zhipu/glm/glm-4-5-air", "name": "GLM-4.5-Air", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 0.2, "completion_price_per_m": 1.1, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1212, "coding_elo": 1212, "swe_bench": 17.1, "humaneval": 71.7, "twelve_cs_score": 52.0, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 11.1, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1118, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 224, "dossier_id": "alibaba/qwen-coder/qwen3-coder-next", "name": "Qwen3-Coder-Next", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1211, "coding_elo": 1211, "swe_bench": 17.0, "humaneval": 71.6, "twelve_cs_score": 67.9, "speed_tps": 482.0, "cosmos_tests": 9, "is_free": false, "what_it_is_for": "Open coding model in the Qwen3-Next architecture, published January 2026.", "summary_intro": "Alibaba model Qwen3-Coder-Next. Status: active. Collected 2026-10-15. Open coding model in the Qwen3-Next architecture, published January 2026.", "benchmarks": { "aa_intelligence_index": 9.2, "aa_coding_index": 36.2, "aa_agentic_index": 0.9, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 225, "dossier_id": "alibaba/qwen/qwen-plus", "name": "Qwen-Plus", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": 0.115, "completion_price_per_m": 0.287, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1211, "coding_elo": 1211, "swe_bench": 16.9, "humaneval": 71.6, "twelve_cs_score": 99.5, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 226, "dossier_id": "arcee/trinity/trinity-large-thinking", "name": "Trinity-Large-Thinking", "maker": "arcee", "family": "arcee/trinity", "context_length": 262144, "prompt_price_per_m": 0.25, "completion_price_per_m": 0.8, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1211, "coding_elo": 1211, "swe_bench": 17.0, "humaneval": 71.6, "twelve_cs_score": 52.0, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Arcee describes it as an agent-first generalist for tool calling, multi-step planning, coding, math, and long reasoning traces.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 227, "dossier_id": "mistral/mistral/mistral-small-4-0-26-03", "name": "Mistral Small 4", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.6, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs_score": 36.1, "speed_tps": 120.0, "cosmos_tests": 9, "is_free": false, "what_it_is_for": "Our powerful hybrid model unifying instruct, reasoning, and coding capabilities in a single model. 119B parameters with 6.5B active.", "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.", "benchmarks": { "aa_intelligence_index": 11.3, "aa_coding_index": 26.6, "aa_agentic_index": 0.8, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 228, "dossier_id": "upstage/solar/solar-pro-3", "name": "Solar Pro 3", "maker": "upstage", "family": "upstage/solar", "context_length": 128000, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1209, "coding_elo": 1209, "swe_bench": 16.5, "humaneval": 71.2, "twelve_cs_score": 99.5, "speed_tps": 120.0, "cosmos_tests": 6, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": 7.8, "aa_coding_index": 16.2, "aa_agentic_index": 1.4, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 229, "dossier_id": "openai/gpt/gpt-6-luna", "name": "GPT-6 Luna", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1207, "coding_elo": 1207, "swe_bench": 16.2, "humaneval": 71.0, "twelve_cs_score": 89.3, "speed_tps": 120.0, "cosmos_tests": 28, "is_free": false, "what_it_is_for": "Our most efficient model for focused, high-volume tasks.", "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.", "benchmarks": { "aa_intelligence_index": 38.1, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 230, "dossier_id": "alibaba/qwen-omni/qwen3-8-omni-flash", "name": "Qwen3.8-Omni-Flash", "maker": "alibaba", "family": "alibaba/qwen-omni", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1207, "coding_elo": 1207, "swe_bench": 16.2, "humaneval": 70.9, "twelve_cs_score": 52.0, "speed_tps": 482.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 231, "dossier_id": "alibaba/qwen/qwen3-8-flash", "name": "Qwen3.8-Flash", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1207, "coding_elo": 1207, "swe_bench": 16.2, "humaneval": 70.9, "twelve_cs_score": 19.6, "speed_tps": 482.0, "cosmos_tests": 132, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 232, "dossier_id": "xiaomi/mimo-v2-6/mimo-v2-6-flash", "name": "MiMo-V2.6-Flash", "maker": "xiaomi", "family": "xiaomi/mimo-v2-6", "context_length": 1048576, "prompt_price_per_m": 0.14, "completion_price_per_m": 0.28, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1204, "coding_elo": 1204, "swe_bench": 15.7, "humaneval": 70.6, "twelve_cs_score": 99.5, "speed_tps": 240.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "Xiaomi recommends it for high-frequency professional work where Pro is more intelligence than the budget allows.", "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.", "benchmarks": { "aa_intelligence_index": 37.9, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 233, "dossier_id": "alibaba/qwen/qwen3-14b", "name": "Qwen3-14B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1203, "coding_elo": 1203, "swe_bench": 15.6, "humaneval": 70.5, "twelve_cs_score": 99.5, "speed_tps": 482.0, "cosmos_tests": 6, "is_free": false, "what_it_is_for": "14B instruction-tuned Qwen3 model.", "summary_intro": "Alibaba model Qwen3-14B. Status: active. Collected 2026-10-15. 14B instruction-tuned Qwen3 model.", "benchmarks": { "aa_intelligence_index": 6.7, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 234, "dossier_id": "mistral/ministral/ministral-3-14b-25-12", "name": "Ministral 3 14B", "maker": "mistral", "family": "mistral/ministral", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.5, "humaneval": 70.4, "twelve_cs_score": 52.0, "speed_tps": 240.0, "cosmos_tests": 6, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 6.0, "aa_coding_index": 14.4, "aa_agentic_index": 1.1, "design_arena_elo": 987, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 235, "dossier_id": "mistral/ministral/ministral-3-8b-25-12", "name": "Ministral 3 8B", "maker": "mistral", "family": "mistral/ministral", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.15, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1202, "coding_elo": 1202, "swe_bench": 15.4, "humaneval": 70.3, "twelve_cs_score": 91.3, "speed_tps": 240.0, "cosmos_tests": 35, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 5.5, "aa_coding_index": 9.7, "aa_agentic_index": 0.6, "design_arena_elo": 1029, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 236, "dossier_id": "cohere/command/command-r-03-2024", "name": "Command R 03-2024", "maker": "cohere", "family": "cohere/command", "context_length": 128000, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1210, "coding_elo": 1200, "swe_bench": 14.0, "humaneval": 71.0, "twelve_cs_score": null, "speed_tps": 110.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": 40.0, "aa_coding_index": 46.0, "aa_agentic_index": 52.0, "design_arena_elo": 1200, "math_500": 46.0, "gpqa_diamond": 31.0, "ifeval": 78.0, "bfcl_tool_calling": 82.0 } }, { "rank": 237, "dossier_id": "ai21/task-specific/summarize", "name": "Summarize", "maker": "ai21", "family": "ai21/task-specific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": 1200, "coding_elo": 1200, "swe_bench": 15.0, "humaneval": 70.0, "twelve_cs_score": 98.0, "speed_tps": 120.0, "cosmos_tests": 0, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 238, "dossier_id": "magic/ltm/ltm-2", "name": "LTM-2", "maker": "magic", "family": "magic/ltm", "context_length": 100000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.\"", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 239, "dossier_id": "magic/ltm/ltm-2-mini", "name": "LTM-2-mini", "maker": "magic", "family": "magic/ltm", "context_length": 100000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 240, "dossier_id": "alibaba/qwen/qwen-long", "name": "Qwen-Long", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 10000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-context Qwen model documented at 10 million tokens. Thinking and tool calling are marked unsupported; structured output is supported.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 241, "dossier_id": "magic/ltm/ltm-1", "name": "LTM-1", "maker": "magic", "family": "magic/ltm", "context_length": 5000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 242, "dossier_id": "google/gemini/gemini-1-5-pro", "name": "Gemini 1.5 Pro", "maker": "google", "family": "google/gemini", "context_length": 2097152, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 243, "dossier_id": "xai/grok/grok-4-1-fast-non-reasoning", "name": "Grok 4.1 Fast Non-Reasoning", "maker": "xai", "family": "xai/grok", "context_length": 2000000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.5, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 244, "dossier_id": "xai/grok/grok-4-1-fast-reasoning", "name": "Grok 4.1 Fast Reasoning", "maker": "xai", "family": "xai/grok", "context_length": 2000000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.5, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 245, "dossier_id": "xai/grok/grok-4-fast-non-reasoning", "name": "Grok 4 Fast Non-Reasoning", "maker": "xai", "family": "xai/grok", "context_length": 2000000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.5, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 246, "dossier_id": "xai/grok/grok-4-fast-reasoning", "name": "Grok 4 Fast Reasoning", "maker": "xai", "family": "xai/grok", "context_length": 2000000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.5, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 247, "dossier_id": "openai/gpt/gpt-5-4", "name": "GPT-5.4", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 2.5, "completion_price_per_m": 15.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A more affordable model for coding and professional work.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 248, "dossier_id": "openai/gpt/gpt-5-4-pro", "name": "GPT-5.4 Pro", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 30.0, "completion_price_per_m": 180.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Version of GPT-5.4 that produces smarter and more precise responses.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 249, "dossier_id": "openai/gpt/gpt-5-5", "name": "GPT-5.5", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 5.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A new class of intelligence for coding and professional work.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 250, "dossier_id": "openai/gpt/gpt-5-5-pro", "name": "GPT-5.5 Pro", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 30.0, "completion_price_per_m": 180.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Version of GPT-5.5 that produces smarter and more precise responses.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 251, "dossier_id": "openai/gpt/gpt-5-6-luna", "name": "GPT-5.6 Luna", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 0.2, "completion_price_per_m": 1.2, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-5.6 model optimized for cost-sensitive workloads", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 252, "dossier_id": "openai/gpt/gpt-5-6-sol", "name": "GPT-5.6 Sol", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 4.0, "completion_price_per_m": 20.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-5.6 flagship model for complex professional work", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 253, "dossier_id": "openai/gpt/gpt-5-6-terra", "name": "GPT-5.6 Terra", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 2.0, "completion_price_per_m": 12.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-5.6 model that balances intelligence and cost", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 254, "dossier_id": "openai/gpt/gpt-6-1-sol", "name": "GPT-6.1 Sol", "maker": "openai", "family": "openai/gpt", "context_length": 1050000, "prompt_price_per_m": 2.0, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Near-Astra performance for complex work at a lower cost.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 255, "dossier_id": "aleph-alpha/kolibri/kolibri-1", "name": "Kolibri-1", "maker": "aleph-alpha", "family": "aleph-alpha/kolibri", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 256, "dossier_id": "berkeley/lwm/lwm-text", "name": "LWM-Text", "maker": "berkeley", "family": "berkeley/lwm", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 257, "dossier_id": "berkeley/lwm/lwm-text-chat", "name": "LWM-Text-Chat", "maker": "berkeley", "family": "berkeley/lwm", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 258, "dossier_id": "google/gemini/gemini-1-5-flash", "name": "Gemini 1.5 Flash", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 259, "dossier_id": "google/gemini/gemini-2-0-flash", "name": "Gemini 2.0 Flash", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 260, "dossier_id": "google/gemini/gemini-2-0-flash-lite", "name": "Gemini 2.0 Flash-Lite", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google described it as the fastest second-generation model, optimized for cost and latency.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 261, "dossier_id": "google/gemini/gemini-2-5-flash", "name": "Gemini 2.5 Flash", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 0.3, "completion_price_per_m": 2.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes it as the price-performance model for low-latency, high-volume work that still needs thinking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 262, "dossier_id": "google/gemini/gemini-2-5-pro", "name": "Gemini 2.5 Pro", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 263, "dossier_id": "google/gemini/gemini-3-1-flash-lite", "name": "Gemini 3.1 Flash-Lite", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 0.25, "completion_price_per_m": 1.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes it as a low-latency, low-cost multimodal model for high-frequency extraction, translation, and routing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 264, "dossier_id": "google/gemini/gemini-3-1-pro", "name": "Gemini 3.1 Pro", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 2.0, "completion_price_per_m": 12.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 265, "dossier_id": "google/gemini/gemini-3-5-flash", "name": "Gemini 3.5 Flash", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 266, "dossier_id": "google/gemini/gemini-3-5-flash-lite", "name": "Gemini 3.5 Flash-Lite", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 0.3, "completion_price_per_m": 2.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google positions it for high-throughput, low-cost subagent tasks and document parsing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 267, "dossier_id": "google/gemini/gemini-3-6-flash", "name": "Gemini 3.6 Flash", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 0.75, "completion_price_per_m": 3.75, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 268, "dossier_id": "google/gemini/gemini-3-7-flash", "name": "Gemini 3.7 Flash", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 269, "dossier_id": "google/gemini/gemini-3-8-flash", "name": "Gemini 3.8 Flash", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 0.75, "completion_price_per_m": 3.75, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 270, "dossier_id": "google/gemini/gemini-3-flash", "name": "Gemini 3 Flash", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": 0.5, "completion_price_per_m": 3.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 271, "dossier_id": "google/gemini/gemini-3-pro", "name": "Gemini 3 Pro", "maker": "google", "family": "google/gemini", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 272, "dossier_id": "google/gemini-deep-research/gemini-deep-research", "name": "Gemini Deep Research", "maker": "google", "family": "google/gemini-deep-research", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 273, "dossier_id": "google/gemini-deep-research/gemini-deep-research-max", "name": "Gemini Deep Research Max", "maker": "google", "family": "google/gemini-deep-research", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 274, "dossier_id": "google/gemini-omni/gemini-omni-1-1-flash", "name": "Gemini Omni Flash", "maker": "google", "family": "google/gemini-omni", "context_length": 1048576, "prompt_price_per_m": 1.5, "completion_price_per_m": 9.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes fast conversational video generation and editing, with native audio, extension, upscaling, and interpolation through the Interactions API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 275, "dossier_id": "iflytek/spark/spark-x2-5-1-7b", "name": "Spark-X2.5-1.7B", "maker": "iflytek", "family": "iflytek/spark", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 276, "dossier_id": "iflytek/spark/spark-x2-5-4b", "name": "Spark-X2.5-4B", "maker": "iflytek", "family": "iflytek/spark", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 277, "dossier_id": "meta/muse/muse-spark-open", "name": "Muse Spark Open", "maker": "meta", "family": "meta/muse", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": 0, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": 1301, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 278, "dossier_id": "meta/muse/muse-wonder-full", "name": "Muse Wonder Full", "maker": "meta", "family": "meta/muse", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 279, "dossier_id": "mit/vila/longvila", "name": "LongVILA", "maker": "mit", "family": "mit/vila", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 280, "dossier_id": "moonshot/kimi/kimi-k2-8-preview", "name": "Kimi K2.8 Preview", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 281, "dossier_id": "moonshot/kimi-linear/kimi-linear-48b-a3b-base", "name": "Kimi-Linear-48B-A3B-Base", "maker": "moonshot", "family": "moonshot/kimi-linear", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 282, "dossier_id": "moonshot/kimi-linear/kimi-linear-48b-a3b-instruct", "name": "Kimi-Linear-48B-A3B-Instruct", "maker": "moonshot", "family": "moonshot/kimi-linear", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 283, "dossier_id": "reflection/beam/beam-1-0-501b-a23b", "name": "Beam", "maker": "reflection", "family": "reflection/beam", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 284, "dossier_id": "xiaomi/mimo-v2/mimo-v2-pro", "name": "MiMo-V2-Pro", "maker": "xiaomi", "family": "xiaomi/mimo-v2", "context_length": 1048576, "prompt_price_per_m": 1.0, "completion_price_per_m": 3.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi describes it as the brain of agent systems for long tool workflows and production engineering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 285, "dossier_id": "yandex/speech/speech-realtime-max-tts-live", "name": "Speech Realtime Max TTS Live", "maker": "yandex", "family": "yandex/speech", "context_length": 1048576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long realtime speech sessions with LiveTTS voices.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 286, "dossier_id": "openai/gpt/gpt-4-1", "name": "GPT-4.1", "maker": "openai", "family": "openai/gpt", "context_length": 1047576, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smartest non-reasoning model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 287, "dossier_id": "openai/gpt/gpt-4-1-mini", "name": "GPT-4.1 Mini", "maker": "openai", "family": "openai/gpt", "context_length": 1047576, "prompt_price_per_m": 0.4, "completion_price_per_m": 1.6, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller, faster version of GPT-4.1", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 288, "dossier_id": "alibaba/qwen/qwen-flash", "name": "Qwen-Flash", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Older low-cost commercial Qwen tier with a 1M context. Still documented as a legacy model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 289, "dossier_id": "alibaba/qwen/qwen3-5-plus", "name": "Qwen3.5-Plus", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": 0.115, "completion_price_per_m": 0.688, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 290, "dossier_id": "alibaba/qwen/qwen3-8-max", "name": "Qwen3.8-Max", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 1000000, "prompt_price_per_m": 2.0, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Alibaba's flagship hosted Qwen3.8 model for agents, coding, and visual understanding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 291, "dossier_id": "alibaba/qwen-coder/qwen3-coder-plus", "name": "Qwen3-Coder-Plus", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": 1000000, "prompt_price_per_m": 1.0, "completion_price_per_m": 5.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 292, "dossier_id": "amazon/nova/nova-2-lite", "name": "Amazon Nova 2 Lite", "maker": "amazon", "family": "amazon/nova", "context_length": 1000000, "prompt_price_per_m": 0.3, "completion_price_per_m": 2.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 293, "dossier_id": "amazon/nova/nova-2-sonic", "name": "Amazon Nova 2 Sonic", "maker": "amazon", "family": "amazon/nova", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 294, "dossier_id": "amazon/nova/nova-premier", "name": "Amazon Nova Premier", "maker": "amazon", "family": "amazon/nova", "context_length": 1000000, "prompt_price_per_m": 2.5, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 295, "dossier_id": "anthropic/mythos/claude-mythos-5", "name": "Claude Mythos 5", "maker": "anthropic", "family": "anthropic/mythos", "context_length": 1000000, "prompt_price_per_m": 10.0, "completion_price_per_m": 50.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 296, "dossier_id": "anthropic/mythos/claude-mythos-5-1", "name": "Claude Mythos 5.1", "maker": "anthropic", "family": "anthropic/mythos", "context_length": 1000000, "prompt_price_per_m": 10.0, "completion_price_per_m": 50.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 297, "dossier_id": "anthropic/mythos/claude-mythos-preview", "name": "Claude Mythos Preview", "maker": "anthropic", "family": "anthropic/mythos", "context_length": 1000000, "prompt_price_per_m": 25.0, "completion_price_per_m": 125.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 298, "dossier_id": "meituan/flash/longcat-flash-lite-sparse", "name": "LongCat-Flash-Lite-Sparse", "maker": "meituan", "family": "meituan/flash", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 299, "dossier_id": "meituan/flash/longcat-flash-thinking-zigzag", "name": "LongCat-Flash-Thinking-ZigZag", "maker": "meituan", "family": "meituan/flash", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 300, "dossier_id": "meituan/longcat/longcat-2-5-preview", "name": "LongCat-2.5-Preview", "maker": "meituan", "family": "meituan/longcat", "context_length": 1000000, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 301, "dossier_id": "minimax/m/minimax-m1-40k", "name": "MiniMax-M1-40k", "maker": "minimax", "family": "minimax/m", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-context reasoning, software engineering, and tool use, with a 40K thinking budget.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 302, "dossier_id": "minimax/m/minimax-m1-80k", "name": "MiniMax-M1-80k", "maker": "minimax", "family": "minimax/m", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The same reasoning jobs as M1-40k, with a larger test-time thinking budget.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 303, "dossier_id": "minimax/m/minimax-m3-1-flash-preview", "name": "MiniMax-M3.1-Flash-Preview", "maker": "minimax", "family": "minimax/m", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 304, "dossier_id": "minimax/minimax-01/minimax-text-01", "name": "MiniMax-Text-01", "maker": "minimax", "family": "minimax/minimax-01", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General language tasks with very long context. It is also the base of MiniMax-VL-01 and MiniMax-M1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 305, "dossier_id": "minimax/minimax-01/minimax-vl-01", "name": "MiniMax-VL-01", "maker": "minimax", "family": "minimax/minimax-01", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image understanding and multimodal chat on top of the MiniMax-01 language model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 306, "dossier_id": "nvidia/nemotron/nemotron-3-5-super-vl", "name": "Nemotron 3.5 Super VL", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 307, "dossier_id": "nvidia/nemotron/nemotron-cascade-2", "name": "Nemotron-Cascade 2", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 308, "dossier_id": "sakana/fugu/fugu", "name": "Fugu", "maker": "sakana", "family": "sakana/fugu", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 309, "dossier_id": "sakana/fugu/fugu-cyber", "name": "Fugu Cyber", "maker": "sakana", "family": "sakana/fugu", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Gated cybersecurity orchestrator for security analysis, vulnerability research, and threat investigation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 310, "dossier_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", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1M-context chat variant. The card says it keeps InternLM2.5-Chat quality at million-word context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 311, "dossier_id": "writer/palmyra-x/palmyra-x6", "name": "Palmyra X6", "maker": "writer", "family": "writer/palmyra-x", "context_length": 1000000, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 312, "dossier_id": "xai/grok/grok-3", "name": "Grok 3", "maker": "xai", "family": "xai/grok", "context_length": 1000000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 313, "dossier_id": "xai/grok/grok-4-20-0309-non-reasoning", "name": "Grok 4.20 Non-Reasoning", "maker": "xai", "family": "xai/grok", "context_length": 1000000, "prompt_price_per_m": 1.25, "completion_price_per_m": 2.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 314, "dossier_id": "zhipu/glm/glm-4-long", "name": "GLM-4-Long", "maker": "zhipu", "family": "zhipu/glm", "context_length": 1000000, "prompt_price_per_m": 1.0, "completion_price_per_m": 1.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 315, "dossier_id": "bytedance/seed/seed-oss", "name": "Seed-OSS-36B", "maker": "bytedance", "family": "bytedance/seed", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 316, "dossier_id": "huawei/openpangu/openpangu-2-0-flash", "name": "openPangu-2.0-Flash", "maker": "huawei", "family": "huawei/openpangu", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller openPangu 2.0 MoE for long-context reasoning and agents, with thinking and non-thinking modes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 317, "dossier_id": "huawei/openpangu/openpangu-2-0-pro", "name": "openPangu-2.0-Pro", "maker": "huawei", "family": "huawei/openpangu", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest openPangu 2.0 MoE, for long-context reasoning and agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 318, "dossier_id": "jina/reader/readerlm-v2", "name": "ReaderLM-v2", "maker": "jina", "family": "jina/reader", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small language model that turns raw HTML into Markdown or schema-following JSON for grounding other models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 319, "dossier_id": "mbzuai/jais-3/jais-3-3-7b", "name": "Jais 3 3.7B", "maker": "mbzuai", "family": "mbzuai/jais-3", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 320, "dossier_id": "mbzuai/jais-3/jais-3-32b", "name": "Jais 3 32B", "maker": "mbzuai", "family": "mbzuai/jais-3", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 321, "dossier_id": "mbzuai/jais-3/jais-3-36b-a4b", "name": "Jais 3 36B-A4B", "maker": "mbzuai", "family": "mbzuai/jais-3", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 322, "dossier_id": "mbzuai/jais-3/jais-3-375b-a23b", "name": "Jais 3 375B-A23B", "maker": "mbzuai", "family": "mbzuai/jais-3", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 323, "dossier_id": "mbzuai/jais-3/jais-3-7b", "name": "Jais 3 7B", "maker": "mbzuai", "family": "mbzuai/jais-3", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 324, "dossier_id": "mbzuai/k2-horizon/k2-horizon-3-7b", "name": "K2 Horizon 3.7B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 325, "dossier_id": "mbzuai/k2-horizon/k2-horizon-32b", "name": "K2 Horizon 32B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 326, "dossier_id": "mbzuai/k2-horizon/k2-horizon-36b-a4b", "name": "K2 Horizon 36B-A4B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 327, "dossier_id": "mbzuai/k2-horizon/k2-horizon-375b-a23b", "name": "K2 Horizon 375B-A23B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 328, "dossier_id": "mbzuai/k2-horizon/k2-horizon-7b", "name": "K2 Horizon 7B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 329, "dossier_id": "princeton/prolong/llama-3-8b-prolong-512k-base", "name": "ProLong-512k-Base", "maker": "princeton", "family": "princeton/prolong", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 330, "dossier_id": "princeton/prolong/llama-3-8b-prolong-512k-instruct", "name": "ProLong-512k-Instruct", "maker": "princeton", "family": "princeton/prolong", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 331, "dossier_id": "tsinghua/minicpm/minicpm-sala", "name": "MiniCPM-SALA", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 524288, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 332, "dossier_id": "nous-research/hermes/hermes-4-3-36b", "name": "Hermes 4.3 36B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": 512000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 333, "dossier_id": "openai/codex/gpt-5-1-codex", "name": "GPT-5.1-Codex", "maker": "openai", "family": "openai/codex", "context_length": 400000, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A version of GPT-5.1 optimized for agentic coding in Codex.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 334, "dossier_id": "openai/codex/gpt-5-1-codex-max", "name": "GPT-5.1-Codex-Max", "maker": "openai", "family": "openai/codex", "context_length": 400000, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A version of GPT-5.1-codex optimized for long running tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 335, "dossier_id": "openai/codex/gpt-5-1-codex-mini", "name": "GPT-5.1-Codex Mini", "maker": "openai", "family": "openai/codex", "context_length": 400000, "prompt_price_per_m": 0.25, "completion_price_per_m": 2.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller, more cost-effective, less-capable version of GPT-5.1-Codex", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 336, "dossier_id": "openai/codex/gpt-5-2-codex", "name": "GPT-5.2-Codex", "maker": "openai", "family": "openai/codex", "context_length": 400000, "prompt_price_per_m": 1.75, "completion_price_per_m": 14.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our most intelligent coding model optimized for long-horizon, agentic coding tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 337, "dossier_id": "openai/codex/gpt-5-3-codex", "name": "GPT-5.3-Codex", "maker": "openai", "family": "openai/codex", "context_length": 400000, "prompt_price_per_m": 1.75, "completion_price_per_m": 14.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The most capable agentic coding model to date.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 338, "dossier_id": "openai/codex/gpt-5-codex", "name": "GPT-5-Codex", "maker": "openai", "family": "openai/codex", "context_length": 400000, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A version of GPT-5 optimized for agentic coding in Codex", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 339, "dossier_id": "openai/gpt/chat-latest", "name": "Chat Latest", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 5.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Latest Instant model used in ChatGPT", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 340, "dossier_id": "openai/gpt/gpt-5-1", "name": "GPT-5.1", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The best model for coding and agentic tasks with configurable reasoning effort", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 341, "dossier_id": "openai/gpt/gpt-5-2", "name": "GPT-5.2", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 1.75, "completion_price_per_m": 14.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Previous flagship model for professional work with configurable reasoning effort", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 342, "dossier_id": "openai/gpt/gpt-5-2-pro", "name": "GPT-5.2 Pro", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 21.0, "completion_price_per_m": 168.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Previous pro model for professional work that produces smarter and more precise responses.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 343, "dossier_id": "openai/gpt/gpt-5-4-mini", "name": "GPT-5.4 Mini", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 0.75, "completion_price_per_m": 4.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our strongest mini model yet for coding, computer use, and subagents", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 344, "dossier_id": "openai/gpt/gpt-5-4-nano", "name": "GPT-5.4 nano", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 0.2, "completion_price_per_m": 1.25, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our cheapest GPT-5.4-class model for simple high-volume tasks", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 345, "dossier_id": "openai/gpt/gpt-5-6-cyber", "name": "GPT-5.6 Cyber", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": 12.5, "completion_price_per_m": 75.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our most advanced cybersecurity model for authorized vulnerability research and security testing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 346, "dossier_id": "openai/gpt/gpt-daybreak-red-latest", "name": "Daybreak Red", "maker": "openai", "family": "openai/gpt", "context_length": 400000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An alias for advanced cybersecurity models for authorized vulnerability research and security testing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 347, "dossier_id": "snowflake/arctic-tilt/arctic-tilt-v1-3", "name": "Arctic-TILT v1.3", "maker": "snowflake", "family": "snowflake/arctic-tilt", "context_length": 400000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 348, "dossier_id": "lighton/orion/orion-mistral", "name": "OriOn-Mistral", "maker": "lighton", "family": "lighton/orion", "context_length": 344064, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 349, "dossier_id": "amazon/nova/nova-pro", "name": "Amazon Nova Pro", "maker": "amazon", "family": "amazon/nova", "context_length": 300000, "prompt_price_per_m": 0.8, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 350, "dossier_id": "amazon/nova/nova-sonic", "name": "Amazon Nova Sonic", "maker": "amazon", "family": "amazon/nova", "context_length": 300000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time, low-latency multi-turn voice conversations for assistants and customer service. Output includes speech plus transcription.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 351, "dossier_id": "01-ai/yi/yi-9b-200k", "name": "Yi-9B-200K", "maker": "01-ai", "family": "01-ai/yi", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-context continuation of Yi-9B, which the maker described as the coding and math standout of the original series.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 352, "dossier_id": "abeja/qwen3/abeja-qwen3-14b-agentic-256k-v0-1", "name": "ABEJA-Qwen3-14B-Agentic-256k-v0.1", "maker": "abeja", "family": "abeja/qwen3", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 353, "dossier_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", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest SEA-LION v4.8 instruct model, for long-context Southeast Asian generation on the Nemotron 3 Super MoE.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 354, "dossier_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", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruct checkpoint of the 30B-A3B SEA-LION MoE, for multilingual Southeast Asian chat and generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 355, "dossier_id": "alibaba/qwen/qwen3-8-2-4t-a95b", "name": "Qwen3.8-2.4T-A95B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 356, "dossier_id": "apple/lensvlm/lensvlm-9b", "name": "LensVLM-9B", "maker": "apple", "family": "apple/lensvlm", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering over long documents represented as compressed images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 357, "dossier_id": "apple/simplesd/simplesd-4b-instruct", "name": "SimpleSD-4B-instruct", "maker": "apple", "family": "apple/simplesd", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation research. The method fine-tunes a model on its own sampled solutions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 358, "dossier_id": "apple/simplesd/simplesd-4b-thinking", "name": "SimpleSD-4B-thinking", "maker": "apple", "family": "apple/simplesd", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation research. The method fine-tunes a model on its own sampled solutions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 359, "dossier_id": "arcee/trinity/trinity-large-base", "name": "Trinity-Large-Base", "maker": "arcee", "family": "arcee/trinity", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A foundation checkpoint for research and fine-tuning, not a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 360, "dossier_id": "arcee/trinity/trinity-large-preview", "name": "Trinity-Large-Preview", "maker": "arcee", "family": "arcee/trinity", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A chat-ready instruct model without the separate reasoning_content channel used by Trinity-Large-Thinking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 361, "dossier_id": "baai/arex/arex-2", "name": "AREX-2", "maker": "baai", "family": "baai/arex", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-horizon self-improvement on coding, machine-learning engineering, and deep research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 362, "dossier_id": "ecnu/skillgym/skillgym-agent", "name": "SkillGym-Agent", "maker": "ecnu", "family": "ecnu/skillgym", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 363, "dossier_id": "epfl/apertus/apertus-1-5-70b", "name": "Apertus 1.5 70B", "maker": "epfl", "family": "epfl/apertus", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 364, "dossier_id": "epfl/apertus/apertus-1-5-8b", "name": "Apertus 1.5 8B", "maker": "epfl", "family": "epfl/apertus", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 365, "dossier_id": "eth/apertus/apertus-1-5-70b", "name": "Apertus 1.5 70B", "maker": "eth", "family": "eth/apertus", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.5 70B (apertus-1-5-70b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 366, "dossier_id": "eth/apertus/apertus-1-5-70b-base", "name": "Apertus 1.5 70B base", "maker": "eth", "family": "eth/apertus", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.5 70B base (apertus-1-5-70b-base). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 367, "dossier_id": "eth/apertus/apertus-1-5-8b", "name": "Apertus 1.5 8B", "maker": "eth", "family": "eth/apertus", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.5 8B (apertus-1-5-8b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 368, "dossier_id": "eth/apertus/apertus-1-5-8b-base", "name": "Apertus 1.5 8B base", "maker": "eth", "family": "eth/apertus", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.5 8B base (apertus-1-5-8b-base). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 369, "dossier_id": "google/gemma/gemma-4-12b", "name": "Gemma 4 12B", "maker": "google", "family": "google/gemma", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 370, "dossier_id": "hku/robofine/robofine-vlm-397b-a17b", "name": "RoboFine-VLM-397B-A17B", "maker": "hku", "family": "hku/robofine", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 371, "dossier_id": "jhu/rankvideo/rankvideo", "name": "RankVideo", "maker": "jhu", "family": "jhu/rankvideo", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 372, "dossier_id": "jina/reader/reader-lm-0-5b", "name": "reader-lm-0.5b", "maker": "jina", "family": "jina/reader", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest ReaderLM, for HTML to Markdown when the 1.5B models are too large.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 373, "dossier_id": "jina/reader/reader-lm-1-5b", "name": "reader-lm-1.5b", "maker": "jina", "family": "jina/reader", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First 1.5B ReaderLM. Converts HTML to Markdown. ReaderLM-v2 replaces it for new work.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 374, "dossier_id": "lg/exaone/exaone-4-5-33b", "name": "EXAONE 4.5 33B", "maker": "lg", "family": "lg/exaone", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-weight vision-language model for documents, charts, industrial images, and the same text reasoning stack as later EXAONE models.", "summary_intro": "EXAONE 4.5 33B LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 375, "dossier_id": "lg/k-exaone/k-exaone-2-0-750b-a37b", "name": "K-EXAONE 2.0 750B-A37B", "maker": "lg", "family": "lg/k-exaone", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "K-EXAONE 2.0 750B-A37B LG AI Research released this as k-exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 376, "dossier_id": "lg/k-exaone/k-exaone-236b-a23b", "name": "K-EXAONE 236B-A23B", "maker": "lg", "family": "lg/k-exaone", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "K-EXAONE 236B-A23B LG AI Research released this as k-exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 377, "dossier_id": "lightblue/jamba/jamba-v0-1-chat-multilingual", "name": "Jamba-v0.1 Chat Multilingual", "maker": "lightblue", "family": "lightblue/jamba", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 378, "dossier_id": "lighton/lightonocr/lightonocr-3-0-8b", "name": "LightOnOCR-3-0.8B", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 379, "dossier_id": "lighton/lightonocr/lightonocr-3-4b", "name": "LightOnOCR-3-4B", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 380, "dossier_id": "lighton/orion/orion-qwen", "name": "OriOn-Qwen", "maker": "lighton", "family": "lighton/orion", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 381, "dossier_id": "lighton/orion/orion-qwen-sr1", "name": "OriOn-Qwen-SR1", "maker": "lighton", "family": "lighton/orion", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 382, "dossier_id": "lighton/rerank/lighton-rerank-lw-0-8b", "name": "LightOn-rerank-LW-0.8B", "maker": "lighton", "family": "lighton/rerank", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 383, "dossier_id": "lighton/rerank/lighton-rerank-lw-2b", "name": "LightOn-rerank-LW-2B", "maker": "lighton", "family": "lighton/rerank", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 384, "dossier_id": "lighton/rerank/lighton-rerank-lw-4b", "name": "LightOn-rerank-LW-4B", "maker": "lighton", "family": "lighton/rerank", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 385, "dossier_id": "lighton/rerank/lighton-rerank-pw-0-8b", "name": "LightOn-rerank-PW-0.8B", "maker": "lighton", "family": "lighton/rerank", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 386, "dossier_id": "lighton/rerank/lighton-rerank-pw-2b", "name": "LightOn-rerank-PW-2B", "maker": "lighton", "family": "lighton/rerank", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 387, "dossier_id": "lighton/rerank/lighton-rerank-pw-4b", "name": "LightOn-rerank-PW-4B", "maker": "lighton", "family": "lighton/rerank", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 388, "dossier_id": "moonshot/kimi/kimi-k2-0905-preview", "name": "Kimi K2 0905", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 262144, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.5, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 389, "dossier_id": "moonshot/kimi/kimi-k2-7-code-highspeed", "name": "Kimi K2.7 Code HighSpeed", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 262144, "prompt_price_per_m": 1.9, "completion_price_per_m": 8.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 390, "dossier_id": "moonshot/kimi/kimi-k2-thinking-turbo", "name": "Kimi K2 Thinking Turbo", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 262144, "prompt_price_per_m": 1.15, "completion_price_per_m": 8.0, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deep reasoning with faster output. The pricing page quotes 60 to 100 tokens per second and a 256K context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 391, "dossier_id": "moonshot/kimi/kimi-k2-turbo-preview", "name": "Kimi K2 Turbo", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 262144, "prompt_price_per_m": 1.15, "completion_price_per_m": 8.0, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster agent and enterprise calls. The pricing page quotes up to 60 tokens per second, with a maximum around 100.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 392, "dossier_id": "patras/maistros/maistros-8b-instruct", "name": "Maistros-8B-Instruct", "maker": "patras", "family": "patras/maistros", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 393, "dossier_id": "perplexity/decider/pplx-decider-v1-1-27b", "name": "pplx-decider-v1.1-27b", "maker": "perplexity", "family": "perplexity/decider", "context_length": 262144, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-decider-v1.1-27b`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 394, "dossier_id": "poolside/laguna/laguna-m-1", "name": "Laguna M.1", "maker": "poolside", "family": "poolside/laguna", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 395, "dossier_id": "poolside/laguna/laguna-m-1-base", "name": "Laguna M.1 base", "maker": "poolside", "family": "poolside/laguna", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 396, "dossier_id": "poolside/laguna/laguna-xs-2", "name": "Laguna XS.2", "maker": "poolside", "family": "poolside/laguna", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 397, "dossier_id": "preferred-networks/plamo-3/plamo-3-0-prime", "name": "PLaMo 3.0 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "context_length": 262144, "prompt_price_per_m": 60.0, "completion_price_per_m": 250.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 398, "dossier_id": "ruc/aewm/aewm", "name": "AEWM", "maker": "ruc", "family": "ruc/aewm", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 399, "dossier_id": "ruc/clawgym/clawgym-30a3", "name": "ClawGym-30A3", "maker": "ruc", "family": "ruc/clawgym", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 400, "dossier_id": "ruc/clawgym/clawgym-4b", "name": "ClawGym-4B", "maker": "ruc", "family": "ruc/clawgym", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 401, "dossier_id": "ruc/swe-master/swe-master-4b-rl", "name": "SWE-Master-4B-RL", "maker": "ruc", "family": "ruc/swe-master", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 402, "dossier_id": "ruc/swe-master/swe-master-4b-sft", "name": "SWE-Master-4B-SFT", "maker": "ruc", "family": "ruc/swe-master", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 403, "dossier_id": "ruc/swe-world/swe-world-4b-rl", "name": "SWE-World-4B-RL", "maker": "ruc", "family": "ruc/swe-world", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 404, "dossier_id": "ruc/swe-world/swe-world-4b-sft", "name": "SWE-World-4B-SFT", "maker": "ruc", "family": "ruc/swe-world", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 405, "dossier_id": "sensetime/sensenova/sensenova-6-7-flash-lite", "name": "SenseNova 6.7 Flash-Lite", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Data analysis, research writeups, and slide workflows, with SenseNova-Skills on GitHub. Skills are a software pack, not a model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 406, "dossier_id": "sensetime/sensenova/sensenova-6-8-flash-lite", "name": "SenseNova 6.8 Flash-Lite", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": 262144, "prompt_price_per_m": 5.0, "completion_price_per_m": 30.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 407, "dossier_id": "sjtu/sr-scientist/sr-scientist-30b", "name": "SR-Scientist-30B", "maker": "sjtu", "family": "sjtu/sr-scientist", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Agentic discovery of scientific equations. The card identifies this file as the RL training checkpoint from the paper.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 408, "dossier_id": "sk-telecom/a-x/a-x-k2", "name": "A.X K2", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X K2 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 409, "dossier_id": "sk-telecom/a-x/a-x-k2-eagle3", "name": "A.X K2 EAGLE3", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X K2 EAGLE3 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 410, "dossier_id": "tencent/hunyuan/hunyuan-large", "name": "Hunyuan-Large", "maker": "tencent", "family": "tencent/hunyuan", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 411, "dossier_id": "tii/falcon-h1/falcon-h1-34b", "name": "Falcon-H1 34B", "maker": "tii", "family": "tii/falcon-h1", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 412, "dossier_id": "tii/falcon-h1/falcon-h1-7b", "name": "Falcon-H1 7B", "maker": "tii", "family": "tii/falcon-h1", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 413, "dossier_id": "tii/falcon-h1-arabic/falcon-h1-arabic-34b", "name": "Falcon-H1-Arabic 34B", "maker": "tii", "family": "tii/falcon-h1-arabic", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic production model for dialect, STEM, code, and long documents. Three sizes shipped as a family.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 414, "dossier_id": "tii/falcon-h1-arabic/falcon-h1-arabic-7b", "name": "Falcon-H1-Arabic 7B", "maker": "tii", "family": "tii/falcon-h1-arabic", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic production model for dialect, STEM, code, and long documents. Three sizes shipped as a family.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 415, "dossier_id": "tii/falcon-h1r/falcon-h1r-7b", "name": "Falcon-H1R 7B", "maker": "tii", "family": "tii/falcon-h1r", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Compact reasoning model for math, code, and instruction following. TII also ships GGUF and FP8 quantizations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 416, "dossier_id": "toronto/bioreason/bioreason-pro-rl", "name": "BioReason-Pro RL", "maker": "toronto", "family": "toronto/bioreason", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "RL checkpoint of the same Qwen3-4B system. The card is shorter. Numeric RL-only scores were not copied.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 417, "dossier_id": "toronto/bioreason/bioreason-pro-sft", "name": "BioReason-Pro SFT", "maker": "toronto", "family": "toronto/bioreason", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 418, "dossier_id": "tsinghua/minicpm-v/minicpm-v-4-6", "name": "MiniCPM-V 4.6", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device image, multi-image, and video understanding with switchable 4x and 16x visual-token compression.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 419, "dossier_id": "tsinghua/minicpm-v/minicpm-v-4-7-1b", "name": "MiniCPM-V 4.7 1B", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The dense 1B MiniCPM-V 4.7 for on-device image, multi-image, document, and video understanding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 420, "dossier_id": "tsinghua/minicpm-v/minicpm-v-4-7-35b-a3b", "name": "MiniCPM-V 4.7 35B-A3B", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 421, "dossier_id": "u-tokyo/weblab-medllm/weblab-medllm-qwen3-235b-instruct", "name": "Weblab-MedLLM-Qwen3-235B-Instruct", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "NEDO Japanese medical LLM for information support. The card forbids direct clinical use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 422, "dossier_id": "u-tokyo/weblab-medllm/weblab-medllm-qwen3-235b-thinking", "name": "Weblab-MedLLM-Qwen3-235B-Thinking", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "NEDO Japanese medical LLM for information support. The card forbids direct clinical use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 423, "dossier_id": "umn/finch/finch-2b", "name": "Finch-2B", "maker": "umn", "family": "umn/finch", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 424, "dossier_id": "umn/finch/finch-4b", "name": "Finch-4B", "maker": "umn", "family": "umn/finch", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 425, "dossier_id": "umn/finch/finch-4b-kto", "name": "Finch-4B-KTO", "maker": "umn", "family": "umn/finch", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 426, "dossier_id": "umn/finch/finch-9b", "name": "Finch-9B", "maker": "umn", "family": "umn/finch", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 427, "dossier_id": "ut-austin/vseek/vseek-em", "name": "VSeek-EM", "maker": "ut-austin", "family": "ut-austin/vseek", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 428, "dossier_id": "ut-austin/vseek/vseek-vetl", "name": "VSeek-VETL", "maker": "ut-austin", "family": "ut-austin/vseek", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 429, "dossier_id": "xiaomi/mimo-v2/mimo-v2-omni", "name": "MiMo-V2-Omni", "maker": "xiaomi", "family": "xiaomi/mimo-v2", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi says it is for agents that must see, hear, and act, not only read text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 430, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5-base", "name": "MiMo-V2.5-Base", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research and further training. The API product is the post-trained MiMo-V2.5.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 431, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5-pro-base", "name": "MiMo-V2.5-Pro-Base", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base weights for research. Xiaomi's agent claims refer to the post-trained checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 432, "dossier_id": "xiaomi/mimo-v2-flash/mimo-v2-flash", "name": "MiMo-V2-Flash", "maker": "xiaomi", "family": "xiaomi/mimo-v2-flash", "context_length": 262144, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 433, "dossier_id": "xiaomi/mimo-v2-flash/mimo-v2-flash-base", "name": "MiMo-V2-Flash-Base", "maker": "xiaomi", "family": "xiaomi/mimo-v2-flash", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi publishes it as the pretraining checkpoint for research. The agent and chat product is MiMo-V2-Flash.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 434, "dossier_id": "zte/nebula-telecom/nebula-tele", "name": "NebulaTele", "maker": "zte", "family": "zte/nebula-telecom", "context_length": 262144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 435, "dossier_id": "twelve-labs/pegasus/pegasus-1-5", "name": "Pegasus 1.5", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "context_length": 261120, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Pegasus 1.5 by Twelve Labs. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 436, "dossier_id": "twelve-labs/pegasus/pegasus-1-6", "name": "Pegasus 1.6", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "context_length": 261120, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Pegasus 1.6 by Twelve Labs. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 437, "dossier_id": "ai21/jamba/jamba", "name": "Jamba", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 438, "dossier_id": "ai21/jamba/jamba-1-5-large", "name": "Jamba 1.5 Large", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 439, "dossier_id": "ai21/jamba/jamba-1-5-mini", "name": "Jamba 1.5 Mini", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.4, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 440, "dossier_id": "ai21/jamba/jamba-1-6-large", "name": "Jamba Large 1.6", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 441, "dossier_id": "ai21/jamba/jamba-1-6-mini", "name": "Jamba Mini 1.6", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.4, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 442, "dossier_id": "ai21/jamba/jamba-1-7-large", "name": "Jamba Large 1.7", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 443, "dossier_id": "ai21/jamba/jamba-1-7-mini", "name": "Jamba Mini 1.7", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.4, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 444, "dossier_id": "ai21/jamba/jamba-instruct", "name": "Jamba-Instruct", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": 0.5, "completion_price_per_m": 0.7, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 445, "dossier_id": "ai21/jamba/jamba-reasoning-3b", "name": "Jamba Reasoning 3B", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 446, "dossier_id": "ai21/jamba/jamba2-3b", "name": "Jamba2 3B", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 447, "dossier_id": "ai21/jamba/jamba2-mini", "name": "Jamba2 Mini", "maker": "ai21", "family": "ai21/jamba", "context_length": 256000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.4, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 448, "dossier_id": "aisingapore/sea-lion/qwen-sea-lion-v4-4b-vl", "name": "Qwen-SEA-LION-v4-4B-VL", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lightweight visual question answering and document reading for Southeast Asian text and scenes, including on consumer hardware.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 449, "dossier_id": "aisingapore/sea-lion/qwen-sea-lion-v4-8b-vl", "name": "Qwen-SEA-LION-v4-8B-VL", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Stronger visual question answering and document analysis than the 4B sibling, still in the small Qwen3-VL range.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 450, "dossier_id": "alibaba/qwen-omni/qwen3-5-omni-plus", "name": "Qwen3.5-Omni-Plus", "maker": "alibaba", "family": "alibaba/qwen-omni", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 451, "dossier_id": "cohere/command/command-a-03-2025", "name": "Command A", "maker": "cohere", "family": "cohere/command", "context_length": 256000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 452, "dossier_id": "cohere/command/command-a-reasoning-08-2025", "name": "Command A Reasoning", "maker": "cohere", "family": "cohere/command", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 453, "dossier_id": "meituan/flash/longcat-flash-lite", "name": "LongCat-Flash-Lite", "maker": "meituan", "family": "meituan/flash", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 454, "dossier_id": "microsoft/mai/mai-code-1-1-flash", "name": "MAI-Code-1.1-Flash", "maker": "microsoft", "family": "microsoft/mai", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 455, "dossier_id": "microsoft/mai/mai-thinking-1", "name": "MAI-Thinking-1", "maker": "microsoft", "family": "microsoft/mai", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Microsoft positions it for enterprise reasoning, software engineering, and math, with function calling and developer instructions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 456, "dossier_id": "nvidia/cosmos/cosmos-reason2", "name": "Cosmos Reason 2", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reason about video and images for embodied tasks, including detection coordinates and explanations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 457, "dossier_id": "nvidia/cosmos/cosmos3-super", "name": "Cosmos 3 Super", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 458, "dossier_id": "ricoh/lmm/lmm-35b-a3b", "name": "\u30ea\u30b3\u30fcLMM_35B-A3B", "maker": "ricoh", "family": "ricoh/lmm", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 459, "dossier_id": "sber/gigachat/gigachat-3-lightning", "name": "GigaChat 3 Lightning", "maker": "sber", "family": "sber/gigachat", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 460, "dossier_id": "shanghai-ai-lab/atria/atria-dawn", "name": "Atria Dawn", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/atria", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same agentic research and engineering role as Atria Dawn Preview, positioned as the full release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 461, "dossier_id": "shanghai-ai-lab/atria/atria-dawn-preview", "name": "Atria Dawn Preview", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/atria", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 462, "dossier_id": "stepfun/step/step-3-5-flash-2603", "name": "Step 3.5 Flash 2603", "maker": "stepfun", "family": "stepfun/step", "context_length": 256000, "prompt_price_per_m": 0.7, "completion_price_per_m": 2.1, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 463, "dossier_id": "stepfun/step/step-3-7-flash", "name": "Step 3.7 Flash", "maker": "stepfun", "family": "stepfun/step", "context_length": 256000, "prompt_price_per_m": 1.35, "completion_price_per_m": 8.1, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 464, "dossier_id": "xai/grok/grok-4", "name": "Grok 4", "maker": "xai", "family": "xai/grok", "context_length": 256000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 465, "dossier_id": "ntu/longva/longva", "name": "LongVA", "maker": "ntu", "family": "ntu/longva", "context_length": 224000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 466, "dossier_id": "minimax/m/minimax-m2-1", "name": "MiniMax-M2.1", "maker": "minimax", "family": "minimax/m", "context_length": 204800, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MiniMax marketed M2.1 for multilingual programming and code refactoring.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 467, "dossier_id": "minimax/m/minimax-m2-1-highspeed", "name": "MiniMax-M2.1-highspeed", "maker": "minimax", "family": "minimax/m", "context_length": 204800, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.4, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower-latency M2.1 serving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 468, "dossier_id": "minimax/m/minimax-m2-5", "name": "MiniMax-M2.5", "maker": "minimax", "family": "minimax/m", "context_length": 204800, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MiniMax says M2.5 targets code generation and refactoring, tool use, search, and office productivity.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 469, "dossier_id": "minimax/m/minimax-m2-5-highspeed", "name": "MiniMax-M2.5-highspeed", "maker": "minimax", "family": "minimax/m", "context_length": 204800, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.4, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower-latency M2.5 calls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 470, "dossier_id": "minimax/m/minimax-m2-7", "name": "MiniMax-M2.7", "maker": "minimax", "family": "minimax/m", "context_length": 204800, "prompt_price_per_m": 0.3, "completion_price_per_m": 1.2, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MiniMax describes M2.7 as the start of recursive self-improvement, aimed at software engineering, office documents, and agent workflows.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 471, "dossier_id": "minimax/m/minimax-m2-7-highspeed", "name": "MiniMax-M2.7-highspeed", "maker": "minimax", "family": "minimax/m", "context_length": 204800, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.4, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower latency serving of M2.7 for coding and agent calls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 472, "dossier_id": "sjtu/davinci/davinci-agency", "name": "daVinci-Agency", "maker": "sjtu", "family": "sjtu/davinci", "context_length": 202752, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 473, "dossier_id": "u-tokyo/weblab-medllm/weblab-medllm-glm-4-7", "name": "Weblab-MedLLM-GLM-4.7", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "context_length": 202752, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same NEDO medical-administration project as the other Weblab-MedLLM releases. Not for direct diagnosis or treatment.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 474, "dossier_id": "01-ai/yi/yi-34b-200k", "name": "Yi-34B-200K", "maker": "01-ai", "family": "01-ai/yi", "context_length": 200000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The maker positions it for long documents. The README equates 200K tokens with roughly 400,000 Chinese characters.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 475, "dossier_id": "01-ai/yi/yi-34b-chat-200k", "name": "Yi-34B-Chat-200K", "maker": "01-ai", "family": "01-ai/yi", "context_length": 200000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 476, "dossier_id": "01-ai/yi/yi-6b-200k", "name": "Yi-6B-200K", "maker": "01-ai", "family": "01-ai/yi", "context_length": 200000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same long-document role as Yi-34B-200K, at the 6B size the maker aimed at personal and academic use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 477, "dossier_id": "01-ai/yi-medium/yi-medium-200k", "name": "Yi-Medium-200K", "maker": "01-ai", "family": "01-ai/yi-medium", "context_length": 200000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 478, "dossier_id": "anthropic/claude/claude-2-1", "name": "Claude 2.1", "maker": "anthropic", "family": "anthropic/claude", "context_length": 200000, "prompt_price_per_m": 8.0, "completion_price_per_m": 24.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 479, "dossier_id": "anthropic/haiku/claude-3-haiku", "name": "Claude Haiku 3", "maker": "anthropic", "family": "anthropic/haiku", "context_length": 200000, "prompt_price_per_m": 0.25, "completion_price_per_m": 1.25, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 480, "dossier_id": "anthropic/opus/claude-opus-4", "name": "Claude Opus 4", "maker": "anthropic", "family": "anthropic/opus", "context_length": 200000, "prompt_price_per_m": 15.0, "completion_price_per_m": 75.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 481, "dossier_id": "anthropic/sonnet/claude-3-7-sonnet", "name": "Claude Sonnet 3.7", "maker": "anthropic", "family": "anthropic/sonnet", "context_length": 200000, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 482, "dossier_id": "anthropic/sonnet/claude-3-sonnet", "name": "Claude Sonnet 3", "maker": "anthropic", "family": "anthropic/sonnet", "context_length": 200000, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 483, "dossier_id": "nous-research/capybara/nous-capybara-34b", "name": "Nous Capybara 34B", "maker": "nous-research", "family": "nous-research/capybara", "context_length": 200000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The long-context 34B Capybara chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 484, "dossier_id": "openai/codex/codex-mini-latest", "name": "codex-mini-latest", "maker": "openai", "family": "openai/codex", "context_length": 200000, "prompt_price_per_m": 1.5, "completion_price_per_m": 6.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast reasoning model optimized for the Codex CLI", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 485, "dossier_id": "openai/o/o3-deep-research", "name": "o3-deep-research", "maker": "openai", "family": "openai/o", "context_length": 200000, "prompt_price_per_m": 10.0, "completion_price_per_m": 40.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our most powerful deep research model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 486, "dossier_id": "openai/o/o4-mini-deep-research", "name": "o4-mini-deep-research", "maker": "openai", "family": "openai/o", "context_length": 200000, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster, more affordable deep research model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 487, "dossier_id": "zhipu/glm/glm-4-7-flashx", "name": "GLM-4.7-FlashX", "maker": "zhipu", "family": "zhipu/glm", "context_length": 200000, "prompt_price_per_m": 0.07, "completion_price_per_m": 0.4, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 488, "dossier_id": "essential-ai/rnj/rnj-1-5-instruct", "name": "Rnj-1.5 Instruct", "maker": "essential-ai", "family": "essential-ai/rnj", "context_length": 163840, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 489, "dossier_id": "01-ai/yi-coder/yi-coder-1-5b", "name": "Yi-Coder-1.5B", "maker": "01-ai", "family": "01-ai/yi-coder", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.5B base code model for local and low-footprint coding tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 490, "dossier_id": "01-ai/yi-coder/yi-coder-1-5b-chat", "name": "Yi-Coder-1.5B-Chat", "maker": "01-ai", "family": "01-ai/yi-coder", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.5B chat code model. The README lists the same 128K window and 52 languages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 491, "dossier_id": "01-ai/yi-coder/yi-coder-9b", "name": "Yi-Coder-9B", "maker": "01-ai", "family": "01-ai/yi-coder", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base code model. The README presents it as a small code model with long-context coding ability.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 492, "dossier_id": "01-ai/yi-coder/yi-coder-9b-chat", "name": "Yi-Coder-9B-Chat", "maker": "01-ai", "family": "01-ai/yi-coder", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat code model. README shows it ahead of several larger code chat models on the maker's multilingual HumanEval average.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 493, "dossier_id": "agh/lapa/lapa-12b", "name": "Lapa-12B", "maker": "agh", "family": "agh/lapa", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 494, "dossier_id": "agh/lapa/lapa-v0-1-instruct", "name": "Lapa-v0.1-Instruct", "maker": "agh", "family": "agh/lapa", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 495, "dossier_id": "alibaba/qwen/qwen2-5-14b", "name": "Qwen2.5-14B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "14B Qwen2.5 instruct model. A 1M-context variant, qwen2.5-14b-instruct-1m, is a separate endpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 496, "dossier_id": "alibaba/qwen/qwen2-5-32b", "name": "Qwen2.5-32B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "32B Qwen2.5 instruct model. Same Model Studio context and max output as the 72B instruct endpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 497, "dossier_id": "alibaba/qwen/qwen2-5-72b", "name": "Qwen2.5-72B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 131072, "prompt_price_per_m": 1.4, "completion_price_per_m": 5.6, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 498, "dossier_id": "alibaba/qwq/qwq-32b", "name": "QwQ-32B", "maker": "alibaba", "family": "alibaba/qwq", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open reasoning model in the Qwen series, trained with supervised fine-tuning and reinforcement learning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 499, "dossier_id": "allenai/tulu/tulu-3-8b", "name": "Tulu 3 8B", "maker": "allenai", "family": "allenai/tulu", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 500, "dossier_id": "apple/diffucoder/diffucoder-7b-base", "name": "DiffuCoder-7B-Base", "maker": "apple", "family": "apple/diffucoder", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation by masked diffusion rather than left-to-right decoding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 501, "dossier_id": "apple/diffucoder/diffucoder-7b-cpgrpo", "name": "DiffuCoder-7B-cpGRPO", "maker": "apple", "family": "apple/diffucoder", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation by masked diffusion rather than left-to-right decoding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 502, "dossier_id": "apple/diffucoder/diffucoder-7b-instruct", "name": "DiffuCoder-7B-Instruct", "maker": "apple", "family": "apple/diffucoder", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation by masked diffusion rather than left-to-right decoding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 503, "dossier_id": "arcee/agent/arcee-agent", "name": "Arcee-Agent", "maker": "arcee", "family": "arcee/agent", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tool routing and function calling at 7B, including use as a small chat agent.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 504, "dossier_id": "arcee/homunculus/homunculus", "name": "Homunculus", "maker": "arcee", "family": "arcee/homunculus", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning traces on a single consumer GPU, per the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 505, "dossier_id": "arcee/maestro/maestro-7b-preview", "name": "Arcee-Maestro-7B-Preview", "maker": "arcee", "family": "arcee/maestro", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Math and coding reasoning at 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 506, "dossier_id": "arcee/meraj/meraj-mini", "name": "Meraj-Mini", "maker": "arcee", "family": "arcee/meraj", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic-first bilingual instruct use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 507, "dossier_id": "arcee/spark/llama-spark", "name": "Llama-Spark", "maker": "arcee", "family": "arcee/spark", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Best-effort conversational model in the 6B to 9B range, per the announcement.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 508, "dossier_id": "arcee/supernova/supernova", "name": "Arcee-SuperNova-v1", "maker": "arcee", "family": "arcee/supernova", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General instruction following as a smaller stand-in for much larger models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 509, "dossier_id": "arcee/supernova/supernova-lite", "name": "Llama-3.1-SuperNova-Lite", "maker": "arcee", "family": "arcee/supernova", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction following at 8B with the SuperNova distillation recipe.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 510, "dossier_id": "arcee/supernova/supernova-medius", "name": "SuperNova-Medius", "maker": "arcee", "family": "arcee/supernova", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction following and reasoning at 14B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 511, "dossier_id": "arcee/trinity/trinity-mini", "name": "Trinity Mini", "maker": "arcee", "family": "arcee/trinity", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 512, "dossier_id": "arcee/trinity/trinity-mini-base", "name": "Trinity Mini Base", "maker": "arcee", "family": "arcee/trinity", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A base checkpoint for domain fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 513, "dossier_id": "arcee/trinity/trinity-nano-base", "name": "Trinity Nano Base", "maker": "arcee", "family": "arcee/trinity", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Domain fine-tuning and research on the small Trinity backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 514, "dossier_id": "arcee/trinity/trinity-nano-preview", "name": "Trinity Nano Preview", "maker": "arcee", "family": "arcee/trinity", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device and edge experiments, not a hosted production endpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 515, "dossier_id": "arcee/virtuoso/virtuoso-large", "name": "Virtuoso-Large", "maker": "arcee", "family": "arcee/virtuoso", "context_length": 131072, "prompt_price_per_m": 0.75, "completion_price_per_m": 1.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cross-domain writing, summarization, and long-context general work.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 516, "dossier_id": "arcee/virtuoso/virtuoso-medium-v2", "name": "Virtuoso-Medium-v2", "maker": "arcee", "family": "arcee/virtuoso", "context_length": 131072, "prompt_price_per_m": 0.5, "completion_price_per_m": 0.8, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mid-size general instruction following at 131K context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 517, "dossier_id": "arcee/virtuoso/virtuoso-small", "name": "Virtuoso-Small", "maker": "arcee", "family": "arcee/virtuoso", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller general instruct model for business text tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 518, "dossier_id": "arcee/virtuoso/virtuoso-small-v2", "name": "Virtuoso-Small-v2", "maker": "arcee", "family": "arcee/virtuoso", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Compact general model with the same distillation recipe as Virtuoso-Medium-v2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 519, "dossier_id": "baichuan/baichuan-m/baichuan-m2-32b", "name": "Baichuan-M2-32B", "maker": "baichuan", "family": "baichuan/baichuan-m", "context_length": 131072, "prompt_price_per_m": 0.002, "completion_price_per_m": 0.02, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 520, "dossier_id": "baichuan/baichuan3/baichuan3-turbo-128k", "name": "Baichuan3-Turbo-128k", "maker": "baichuan", "family": "baichuan/baichuan3", "context_length": 131072, "prompt_price_per_m": 0.024, "completion_price_per_m": 0.024, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 521, "dossier_id": "baidu/ernie/ernie-4-5-21b-a3b", "name": "ERNIE-4.5-21B-A3B", "maker": "baidu", "family": "baidu/ernie", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 522, "dossier_id": "baidu/ernie/ernie-4-5-21b-a3b-thinking", "name": "ERNIE-4.5-21B-A3B-Thinking", "maker": "baidu", "family": "baidu/ernie", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 523, "dossier_id": "baidu/ernie/ernie-4-5-300b-a47b", "name": "ERNIE-4.5-300B-A47B", "maker": "baidu", "family": "baidu/ernie", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 524, "dossier_id": "baidu/ernie/ernie-4-5-vl-28b-a3b", "name": "ERNIE-4.5-VL-28B-A3B", "maker": "baidu", "family": "baidu/ernie", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Visual perception, documents, charts, and visual reasoning, with a thinking mode and a non-thinking mode.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 525, "dossier_id": "bar-ilan/dictalm/dictalm-3-0-12b", "name": "DictaLM 3.0 12B", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 526, "dossier_id": "bar-ilan/dictalm/dictalm-3-0-12b-instruct", "name": "DictaLM 3.0 12B Instruct", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "12B instruct model. The release page prints Hebrew chat scores.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 527, "dossier_id": "dlut/rexdrug/rexdrug", "name": "RexDrug", "maker": "dlut", "family": "dlut/rexdrug", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 528, "dossier_id": "dlut/taiyi/taiyi-2", "name": "Taiyi 2", "maker": "dlut", "family": "dlut/taiyi", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 529, "dossier_id": "elyza/diffusion/elyza-diffusion-base-1-0-dream-7b", "name": "ELYZA-Diffusion-Base-1.0-Dream-7B", "maker": "elyza", "family": "elyza/diffusion", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research and further tuning of a Japanese diffusion language model. ELYZA says to use the instruct checkpoint for applications.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 530, "dossier_id": "elyza/diffusion/elyza-diffusion-instruct-1-0-dream-7b", "name": "ELYZA-Diffusion-Instruct-1.0-Dream-7B", "maker": "elyza", "family": "elyza/diffusion", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese instruction following and chat where parallel token filling is useful. ELYZA also pointed at a Hugging Face chat demo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 531, "dossier_id": "elyza/thinking/elyza-thinking-1-0-qwen-32b", "name": "ELYZA-Thinking-1.0-Qwen-32B", "maker": "elyza", "family": "elyza/thinking", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 532, "dossier_id": "google/gemini-image/gemini-3-1-flash-image", "name": "Nano Banana 2", "maker": "google", "family": "google/gemini-image", "context_length": 131072, "prompt_price_per_m": 0.5, "completion_price_per_m": 3.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 533, "dossier_id": "google/gemini-image/gemini-nano-banana-2-1", "name": "Nano Banana 2.1", "maker": "google", "family": "google/gemini-image", "context_length": 131072, "prompt_price_per_m": 1.5, "completion_price_per_m": 7.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 534, "dossier_id": "google/gemini-live/gemini-2-5-flash-live", "name": "Gemini 2.5 Flash Live", "maker": "google", "family": "google/gemini-live", "context_length": 131072, "prompt_price_per_m": 0.5, "completion_price_per_m": 2.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes it as the Live API model for low-latency bidirectional voice and video, with native audio reasoning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 535, "dossier_id": "google/gemini-live/gemini-3-1-flash-live", "name": "Gemini 3.1 Flash Live", "maker": "google", "family": "google/gemini-live", "context_length": 131072, "prompt_price_per_m": 0.75, "completion_price_per_m": 4.5, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 536, "dossier_id": "google/gemini-live/gemini-3-5-live-translate", "name": "Gemini 3.5 Live Translate", "maker": "google", "family": "google/gemini-live", "context_length": 131072, "prompt_price_per_m": 3.5, "completion_price_per_m": 21.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 537, "dossier_id": "google/gemini-live/gemini-3-8-live", "name": "Gemini 3.8 Live", "maker": "google", "family": "google/gemini-live", "context_length": 131072, "prompt_price_per_m": 0.75, "completion_price_per_m": 4.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 538, "dossier_id": "google/gemini-live/gemini-3-8-live-extended-thinking", "name": "Gemini 3.8 Live Extended Thinking", "maker": "google", "family": "google/gemini-live", "context_length": 131072, "prompt_price_per_m": 0.75, "completion_price_per_m": 4.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google recommends it when a voice session needs background reasoning and asynchronous tool calls while audio keeps streaming.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 539, "dossier_id": "google/gemini-robotics/gemini-robotics-er-1-6", "name": "Gemini Robotics-ER 1.6", "maker": "google", "family": "google/gemini-robotics", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google says it interprets visual scenes, reasons spatially, and plans robot actions from language, including instrument reading.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 540, "dossier_id": "google/gemini-robotics/gemini-robotics-er-2", "name": "Gemini Robotics-ER 2", "maker": "google", "family": "google/gemini-robotics", "context_length": 131072, "prompt_price_per_m": 1.0, "completion_price_per_m": 5.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google says it does spatial reasoning, video understanding, tool orchestration, and multi-robot coordination from text, image, video, and audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 541, "dossier_id": "google/gemini-robotics/gemini-robotics-er-2-streaming", "name": "Gemini Robotics-ER 2 Streaming", "maker": "google", "family": "google/gemini-robotics", "context_length": 131072, "prompt_price_per_m": 1.0, "completion_price_per_m": 5.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes it as the streaming embodied-reasoning endpoint, with bidirectional Live API function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 542, "dossier_id": "google/gemma/gemma-4-e2b", "name": "Gemma 4 E2B", "maker": "google", "family": "google/gemma", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 543, "dossier_id": "google/gemma/gemma-4-e4b", "name": "Gemma 4 E4B", "maker": "google", "family": "google/gemma", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 544, "dossier_id": "hit/kalm/kalm-reranker-v1-large", "name": "KaLM-Reranker-V1-Large", "maker": "hit", "family": "hit/kalm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 545, "dossier_id": "hit/kalm/kalm-reranker-v1-nano", "name": "KaLM-Reranker-V1-Nano", "maker": "hit", "family": "hit/kalm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 546, "dossier_id": "hit/kalm/kalm-reranker-v1-small", "name": "KaLM-Reranker-V1-Small", "maker": "hit", "family": "hit/kalm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 547, "dossier_id": "huawei/openpangu/openpangu-r-72b-2512", "name": "openPangu-R-72B-2512", "maker": "huawei", "family": "huawei/openpangu", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "72B-class MoE reasoning model with 15B activated parameters and a 128k context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 548, "dossier_id": "huawei/openpangu/openpangu-r-7b-2512", "name": "openPangu-R-7B-2512", "maker": "huawei", "family": "huawei/openpangu", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B dense reasoning model with a 128k context and a switch between fast and slow thinking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 549, "dossier_id": "huawei/openpangu/openpangu-vl-7b", "name": "openPangu-VL-7B", "maker": "huawei", "family": "huawei/openpangu", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast-thinking vision-language model for dialogue, documents, grounding, counting, and video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 550, "dossier_id": "huawei/pangu-industry/pangu-finance-nlp-reasoner-n2-128k", "name": "Pangu-Finance-NLP-Reasoner-N2-128K", "maker": "huawei", "family": "huawei/pangu-industry", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Financial slow-thinking NLP at 128K inference.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 551, "dossier_id": "huawei/pangu-nlp/pangu-nky-nlp-n2-128k", "name": "Pangu-NKY-NLP-N2-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "April 2026 fast-thinking NKY N2 model at 128K, with continued pretraining and SFT.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 552, "dossier_id": "huawei/pangu-nlp/pangu-nky-nlp-n2-reasoner-128k", "name": "Pangu-NKY-NLP-N2-Reasoner-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "April 2026 slow-thinking NKY N2 reasoner at 128K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 553, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n1-128k", "name": "Pangu-NLP-N1-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast-thinking N1 NLP model for 128K inference, without training marked on the capability table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 554, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n1-reasoner-128k", "name": "Pangu-NLP-N1-Reasoner-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slow-thinking N1 reasoner for 128K training and inference.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 555, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n2-128k", "name": "Pangu-NLP-N2-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "N2 NLP at a stated 128K sequence length. Training is not marked for these rows.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 556, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n2-reasoner-128k", "name": "Pangu-NLP-N2-Reasoner-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slow-thinking N2 reasoner with 128K training and inference support marked.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 557, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n4-reasoner-128k", "name": "Pangu-NLP-N4-Reasoner-128K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slow-thinking N4 reasoner for 128K inference only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 558, "dossier_id": "huggingface/idefics3/idefics3-8b", "name": "Idefics3-8B-Llama3", "maker": "huggingface", "family": "huggingface/idefics3", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face's Idefics3 8B model for documents, OCR, and visual reasoning, with a Llama 3.1 8B language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 559, "dossier_id": "huji/sims/sims-7b", "name": "SIMS-7B", "maker": "huji", "family": "huji/sims", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 560, "dossier_id": "huji/sims/sims-llama3-2-3b", "name": "SIMS-Llama3.2-3B", "maker": "huji", "family": "huji/sims", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 561, "dossier_id": "huji/slam/slam-large", "name": "Slam Large", "maker": "huji", "family": "huji/slam", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 562, "dossier_id": "ibm/granite/granite-3-1-1b-a400m-base", "name": "Granite-3.1-1B-A400M-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 563, "dossier_id": "ibm/granite/granite-3-1-1b-a400m-instruct", "name": "Granite-3.1-1B-A400M-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 564, "dossier_id": "ibm/granite/granite-3-1-2b-base", "name": "Granite-3.1-2B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 565, "dossier_id": "ibm/granite/granite-3-1-2b-instruct", "name": "Granite-3.1-2B-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 566, "dossier_id": "ibm/granite/granite-3-1-3b-a800m-base", "name": "Granite-3.1-3B-A800M-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 567, "dossier_id": "ibm/granite/granite-3-1-3b-a800m-instruct", "name": "Granite-3.1-3B-A800M-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 568, "dossier_id": "ibm/granite/granite-3-1-8b-base", "name": "Granite-3.1-8B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 569, "dossier_id": "ibm/granite/granite-3-1-8b-instruct", "name": "Granite-3.1-8B-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 570, "dossier_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", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 571, "dossier_id": "ibm/granite/granite-3-2-2b-instruct", "name": "Granite-3.2-2B-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 572, "dossier_id": "ibm/granite/granite-3-2-8b-instruct", "name": "Granite-3.2-8B-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 573, "dossier_id": "ibm/granite/granite-3-2-8b-instruct-preview", "name": "Granite-3.2-8B-Instruct-Preview", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 574, "dossier_id": "ibm/granite/granite-3-3-2b-base", "name": "Granite-3.3-2B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 575, "dossier_id": "ibm/granite/granite-3-3-2b-instruct", "name": "Granite-3.3-2B-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 576, "dossier_id": "ibm/granite/granite-3-3-8b-base", "name": "Granite-3.3-8B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 577, "dossier_id": "ibm/granite/granite-3-3-8b-instruct", "name": "Granite-3.3-8B-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.2, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 578, "dossier_id": "ibm/granite/granite-3-3-8b-math-prm-v2", "name": "Granite-3.3-8B-Math-PRM-v2", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 579, "dossier_id": "ibm/granite/granite-4-0-1b", "name": "Granite-4.0-1B", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 580, "dossier_id": "ibm/granite/granite-4-0-1b-base", "name": "Granite-4.0-1B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 581, "dossier_id": "ibm/granite/granite-4-0-h-1b", "name": "Granite-4.0-H-1B", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 582, "dossier_id": "ibm/granite/granite-4-0-h-1b-base", "name": "Granite-4.0-H-1B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 583, "dossier_id": "ibm/granite/granite-4-0-h-micro-base", "name": "Granite-4.0-H-Micro-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 584, "dossier_id": "ibm/granite/granite-4-0-h-small", "name": "Granite-4.0-H-Small", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": 0.0636, "completion_price_per_m": 0.265, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 585, "dossier_id": "ibm/granite/granite-4-0-h-small-base", "name": "Granite-4.0-H-Small-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 586, "dossier_id": "ibm/granite/granite-4-0-h-tiny", "name": "Granite-4.0-H-Tiny", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 587, "dossier_id": "ibm/granite/granite-4-0-h-tiny-base", "name": "Granite-4.0-H-Tiny-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 588, "dossier_id": "ibm/granite/granite-4-0-micro", "name": "Granite-4.0-Micro", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 589, "dossier_id": "ibm/granite/granite-4-0-micro-base", "name": "Granite-4.0-Micro-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 590, "dossier_id": "ibm/granite/granite-4-0-tiny-base-preview", "name": "Granite-4.0-Tiny-Base-Preview", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 591, "dossier_id": "ibm/granite/granite-4-0-tiny-preview", "name": "Granite-4.0-Tiny-Preview", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 592, "dossier_id": "ibm/granite/granite-4-1-30b", "name": "Granite-4.1-30B", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 593, "dossier_id": "ibm/granite/granite-4-1-30b-base", "name": "Granite-4.1-30B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 594, "dossier_id": "ibm/granite/granite-4-1-3b", "name": "Granite-4.1-3B", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 595, "dossier_id": "ibm/granite/granite-4-1-3b-base", "name": "Granite-4.1-3B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 596, "dossier_id": "ibm/granite/granite-4-1-8b", "name": "Granite-4.1-8B", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 597, "dossier_id": "ibm/granite/granite-4-1-8b-base", "name": "Granite-4.1-8B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 598, "dossier_id": "ibm/granite/granite-4-2-30b", "name": "Granite-4.2-30B", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 599, "dossier_id": "ibm/granite/granite-4-2-3b", "name": "Granite-4.2-3B", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 600, "dossier_id": "ibm/granite/granite-switch-4-1-30b-preview", "name": "Granite Switch 4.1 30B Preview", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 601, "dossier_id": "ibm/granite/granite-switch-4-1-3b-preview", "name": "Granite Switch 4.1 3B Preview", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 602, "dossier_id": "ibm/granite/granite-switch-4-1-8b-preview", "name": "Granite Switch 4.1 8B Preview", "maker": "ibm", "family": "ibm/granite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 603, "dossier_id": "ibm/granite-guardian/granite-guardian-3-1-2b", "name": "Granite Guardian 3.1 2B", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 604, "dossier_id": "ibm/granite-guardian/granite-guardian-3-1-8b", "name": "Granite Guardian 3.1 8B", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 605, "dossier_id": "ibm/granite-guardian/granite-guardian-3-2-3b-a800m", "name": "Granite Guardian 3.2 3B-A800M", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 606, "dossier_id": "ibm/granite-guardian/granite-guardian-3-2-5b", "name": "Granite Guardian 3.2 5B", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 607, "dossier_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", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 608, "dossier_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", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 609, "dossier_id": "ibm/granite-guardian/granite-guardian-3-3-8b", "name": "Granite Guardian 3.3 8B", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 610, "dossier_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", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 611, "dossier_id": "ibm/granite-guardian/granite-guardian-4-1-8b", "name": "Granite Guardian 4.1 8B", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 612, "dossier_id": "ibm/granite-vision/granite-4-0-3b-vision", "name": "Granite 4.0 3B Vision", "maker": "ibm", "family": "ibm/granite-vision", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 613, "dossier_id": "ibm/granite-vision/granite-vision-3-2-2b", "name": "granite-vision-3.2-2b", "maker": "ibm", "family": "ibm/granite-vision", "context_length": 131072, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 614, "dossier_id": "ibm/granite-vision/granite-vision-3-3-2b", "name": "granite-vision-3.3-2b", "maker": "ibm", "family": "ibm/granite-vision", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 615, "dossier_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", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 616, "dossier_id": "ibm/granite-vision/granite-vision-3-3-2b-embedding", "name": "granite-vision-3.3-2b-embedding", "maker": "ibm", "family": "ibm/granite-vision", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 617, "dossier_id": "ibm/granite-vision/granite-vision-4-1-4b", "name": "Granite-Vision-4.1-4B", "maker": "ibm", "family": "ibm/granite-vision", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 618, "dossier_id": "iflytek/spark/spark-pro-128k", "name": "Spark Pro-128K", "maker": "iflytek", "family": "iflytek/spark", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 619, "dossier_id": "jina/reranker/jina-reranker-v3", "name": "jina-reranker-v3", "maker": "jina", "family": "jina/reranker", "context_length": 131072, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Listwise multilingual reranker. Still served. New projects are told to use v3.5.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 620, "dossier_id": "jina/reranker/jina-reranker-v3-5", "name": "jina-reranker-v3.5", "maker": "jina", "family": "jina/reranker", "context_length": 131072, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Current text reranker. Listwise, multilingual, and the model Jina tells you to switch to from v3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 621, "dossier_id": "kaist/kormo/kormo-10b-base", "name": "KORMo-10B-base", "maker": "kaist", "family": "kaist/kormo", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 622, "dossier_id": "kaist/kormo/kormo-10b-sft", "name": "KORMo-10B-sft", "maker": "kaist", "family": "kaist/kormo", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 623, "dossier_id": "korea-university/kullm/empathetickullm", "name": "EmpatheticKULLM", "maker": "korea-university", "family": "korea-university/kullm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not stated. The repository name is the only clue, and it is not treated as a specification.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 624, "dossier_id": "lg/exaone/exaone-4-0-32b", "name": "EXAONE 4.0 32B", "maker": "lg", "family": "lg/exaone", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hybrid model that switches between a normal answer and a thinking mode, with tool calling, for professional workloads.", "summary_intro": "EXAONE 4.0 32B LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 625, "dossier_id": "lightblue/r1/deepseek-r1-distill-qwen-1-5b-multilingual", "name": "DeepSeek-R1-Distill-Qwen-1.5B Multilingual", "maker": "lightblue", "family": "lightblue/r1", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 626, "dossier_id": "lightblue/r1/deepseek-r1-distill-qwen-14b-multilingual", "name": "DeepSeek-R1-Distill-Qwen-14B Multilingual", "maker": "lightblue", "family": "lightblue/r1", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 627, "dossier_id": "lightblue/r1/deepseek-r1-distill-qwen-7b-japanese", "name": "DeepSeek-R1-Distill-Qwen-7B Japanese", "maker": "lightblue", "family": "lightblue/r1", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 628, "dossier_id": "lightblue/r1/deepseek-r1-distill-qwen-7b-multilingual", "name": "DeepSeek-R1-Distill-Qwen-7B Multilingual", "maker": "lightblue", "family": "lightblue/r1", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 629, "dossier_id": "lighton/colgrep/colgrep-agent-2b", "name": "ColGrep-Agent-2B", "maker": "lighton", "family": "lighton/colgrep", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 630, "dossier_id": "liquid-ai/lfm2-5/lfm2-5-24b-a2b", "name": "LFM2.5-24B-A2B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "context_length": 131072, "prompt_price_per_m": 0.05, "completion_price_per_m": 0.7, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 631, "dossier_id": "lisbon/gervasio/gervasio-70b-ptpt", "name": "Gerv\u00e1sio 70B PT-PT", "maker": "lisbon", "family": "lisbon/gervasio", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 632, "dossier_id": "lisbon/gervasio/gervasio-8b-ptpt", "name": "Gerv\u00e1sio 8B PT-PT", "maker": "lisbon", "family": "lisbon/gervasio", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 633, "dossier_id": "ljubljana/gams/gams3-12b", "name": "GaMS3-12B", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 634, "dossier_id": "ljubljana/gams/gams3-12b-grpo-translator", "name": "GaMS3-12B-GRPO-Translator", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-to-Slovene translation with the professional-translator prompt printed on the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 635, "dossier_id": "ljubljana/gams/gams3-12b-instruct", "name": "GaMS3-12B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 636, "dossier_id": "ljubljana/gams/gams3-12b-instruct-lex", "name": "GaMS3-12B-Instruct-Lex", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 637, "dossier_id": "ljubljana/gams/gams3-12b-multimodal", "name": "GaMS3-12B-Multimodal", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene visual question answering. The only demonstrated task is asking which city is in an image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 638, "dossier_id": "mbzuai/jais-3/jais-3-0-9b", "name": "Jais 3 0.9B", "maker": "mbzuai", "family": "mbzuai/jais-3", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 639, "dossier_id": "mbzuai/k2-horizon/k2-horizon-0-9b", "name": "K2 Horizon 0.9B", "maker": "mbzuai", "family": "mbzuai/k2-horizon", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 640, "dossier_id": "microsoft/phi/phi-3-5-moe", "name": "Phi-3.5-MoE", "maker": "microsoft", "family": "microsoft/phi", "context_length": 131072, "prompt_price_per_m": 0.00016, "completion_price_per_m": 0.00064, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 641, "dossier_id": "microsoft/phi/phi-4-mini", "name": "Phi-4-mini", "maker": "microsoft", "family": "microsoft/phi", "context_length": 131072, "prompt_price_per_m": 0.00075, "completion_price_per_m": 0.0003, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 642, "dossier_id": "microsoft/phi/phi-4-multimodal", "name": "Phi-4-multimodal", "maker": "microsoft", "family": "microsoft/phi", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Microsoft positions it for speech recognition, translation, summarization, question answering, and audio understanding, plus image and text instructions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 643, "dossier_id": "moonshot/kimi/kimi-k2-0711-preview", "name": "Kimi K2 Instruct", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 131072, "prompt_price_per_m": 0.6, "completion_price_per_m": 2.5, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General chat and agent tool use without a long-thinking mode. The card calls it reflex-grade.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 644, "dossier_id": "moonshot/kimi/kimi-k2-base", "name": "Kimi K2 Base", "maker": "moonshot", "family": "moonshot/kimi", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 645, "dossier_id": "moonshot/kimi-vl/kimi-vl-a3b-instruct", "name": "Kimi-VL-A3B-Instruct", "maker": "moonshot", "family": "moonshot/kimi-vl", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruct variant for perception, OCR, long video and documents, and agent use. Recommended temperature 0.2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 646, "dossier_id": "moonshot/kimi-vl/kimi-vl-a3b-thinking", "name": "Kimi-VL-A3B-Thinking", "maker": "moonshot", "family": "moonshot/kimi-vl", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-thinking variant. Recommended temperature 0.8.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 647, "dossier_id": "moonshot/kimi-vl/kimi-vl-a3b-thinking-2506", "name": "Kimi-VL-A3B-Thinking-2506", "maker": "moonshot", "family": "moonshot/kimi-vl", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "June 2025 long-CoT update of Kimi-VL-Thinking, trained with SFT and reinforcement learning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 648, "dossier_id": "moonshot/moonshot-v1/moonshot-v1-128k", "name": "Moonshot v1 128K", "maker": "moonshot", "family": "moonshot/moonshot-v1", "context_length": 131072, "prompt_price_per_m": 10.0, "completion_price_per_m": 30.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Longest fixed v1 cap. Same model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 649, "dossier_id": "moonshot/moonshot-v1/moonshot-v1-128k-vision-preview", "name": "Moonshot v1 128K Vision", "maker": "moonshot", "family": "moonshot/moonshot-v1", "context_length": 131072, "prompt_price_per_m": 10.0, "completion_price_per_m": 30.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision preview at the 128K cap.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 650, "dossier_id": "moonshot/moonshot-v1/moonshot-v1-auto", "name": "Moonshot v1 Auto", "maker": "moonshot", "family": "moonshot/moonshot-v1", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 651, "dossier_id": "neu/gram/gram-llama-3-2-3b", "name": "GRAM-Llama-3.2-3B", "maker": "neu", "family": "neu/gram", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 652, "dossier_id": "nous-research/hermes/hermes-3-llama-3-1-405b", "name": "Hermes 3 Llama 3.1 405B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": 131072, "prompt_price_per_m": 1.0, "completion_price_per_m": 1.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 653, "dossier_id": "nous-research/hermes/hermes-3-llama-3-1-70b", "name": "Hermes 3 Llama 3.1 70B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": 131072, "prompt_price_per_m": 0.7, "completion_price_per_m": 0.7, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 654, "dossier_id": "nous-research/hermes/hermes-3-llama-3-1-8b", "name": "Hermes 3 Llama 3.1 8B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 655, "dossier_id": "nous-research/hermes/hermes-4-70b", "name": "Hermes 4 70B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The mid-size Hermes 4 hybrid reasoner. The releases index says it shares the 405B improvements at 70B parameters.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 656, "dossier_id": "nous-research/yarn/yarn-llama-2-13b-128k", "name": "Yarn Llama 2 13B 128k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 657, "dossier_id": "nous-research/yarn/yarn-mistral-7b-128k", "name": "Yarn Mistral 7B 128k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 658, "dossier_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", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 659, "dossier_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", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 660, "dossier_id": "nvidia/mistral-nemo/mistral-nemo-12b", "name": "Mistral NeMo 12B Instruct", "maker": "nvidia", "family": "nvidia/mistral-nemo", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 661, "dossier_id": "nvidia/nemotron/nemotron-nano-12b-v2", "name": "Nemotron Nano 12B v2", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same job as Nano 9B v2, with more capacity: multilingual instruct and reasoning at 128K context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 662, "dossier_id": "nvidia/nemotron/nemotron-nano-9b-v2", "name": "Nemotron Nano 9B v2", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 663, "dossier_id": "prime-intellect/intellect/intellect-3", "name": "INTELLECT-3", "maker": "prime-intellect", "family": "prime-intellect/intellect", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 664, "dossier_id": "prime-intellect/intellect/intellect-3-1", "name": "INTELLECT-3.1", "maker": "prime-intellect", "family": "prime-intellect/intellect", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 665, "dossier_id": "rakuten/rakuten-ai-2/rakuten-ai-2-0-mini", "name": "Rakuten AI 2.0 mini", "maker": "rakuten", "family": "rakuten/rakuten-ai-2", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 666, "dossier_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", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 667, "dossier_id": "rinna/bakeneko/deepseek-r1-distill-qwen2-5-bakeneko-32b", "name": "DeepSeek R1 Distill Qwen2.5 Bakeneko 32B", "maker": "rinna", "family": "rinna/bakeneko", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning model: chat-vector merge of Bakeneko 32B with DeepSeek-R1-Distill-Qwen-32B, then ORPO on DeepSeek-R1 traces.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 668, "dossier_id": "rinna/bakeneko/qwen2-5-bakeneko-32b", "name": "Qwen2.5 Bakeneko 32B", "maker": "rinna", "family": "rinna/bakeneko", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pretrain of Qwen2.5-32B on about 18B tokens. Bakeneko is the youkai \u5316\u3051\u732b.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 669, "dossier_id": "rinna/bakeneko/qwq-bakeneko-32b", "name": "QwQ Bakeneko 32B", "maker": "rinna", "family": "rinna/bakeneko", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning model: chat-vector merge of Bakeneko 32B with QwQ-32B, then ORPO on DeepSeek-R1 traces.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 670, "dossier_id": "ruc/deepanalyze/deepanalyze-8b", "name": "DeepAnalyze-8B", "maker": "ruc", "family": "ruc/deepanalyze", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 671, "dossier_id": "ruc/simpledeepsearcher/simpledeepsearcher-qwq-32b", "name": "SimpleDeepSearcher QwQ-32B", "maker": "ruc", "family": "ruc/simpledeepsearcher", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 672, "dossier_id": "ruc/still/still-3-1-5b-preview", "name": "STILL-3-1.5B-preview", "maker": "ruc", "family": "ruc/still", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 673, "dossier_id": "ruc/still/still-3-tool-32b", "name": "STILL-3-Tool-32B", "maker": "ruc", "family": "ruc/still", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 674, "dossier_id": "ruc/tablellm/tablellm-8b", "name": "TableLLM-8B", "maker": "ruc", "family": "ruc/tablellm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 675, "dossier_id": "salesforce/xgen/xgen-small", "name": "xGen-small", "maker": "salesforce", "family": "salesforce/xgen", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 676, "dossier_id": "sber/gigachat/gigachat-3-1-ultra", "name": "GigaChat 3.1 Ultra", "maker": "sber", "family": "sber/gigachat", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 677, "dossier_id": "sensetime/sensechat/sensechat-128k", "name": "SenseChat-128K", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-document chat in the V4 generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 678, "dossier_id": "sensetime/sensechat/sensechat-5", "name": "SenseChat-5", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 131072, "prompt_price_per_m": 8.0, "completion_price_per_m": 20.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 679, "dossier_id": "sensetime/sensenova/sensenova-v6-5-pro", "name": "SenseNova V6.5 Pro", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": 131072, "prompt_price_per_m": 3.0, "completion_price_per_m": 9.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 680, "dossier_id": "sensetime/sensenova/sensenova-v6-5-turbo", "name": "SenseNova V6.5 Turbo", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": 131072, "prompt_price_per_m": 1.5, "completion_price_per_m": 4.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower-cost image, text, and video understanding with text output.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 681, "dossier_id": "sjtu/davinci/davinci-origin-7b", "name": "daVinci-origin-7B", "maker": "sjtu", "family": "sjtu/davinci", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same clean-room pretraining baseline as the 3B model, at 7 billion parameters as stated on the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 682, "dossier_id": "sjtu/limi/limi", "name": "LIMI", "maker": "sjtu", "family": "sjtu/limi", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 683, "dossier_id": "sjtu/limi/limi-air", "name": "LIMI-Air", "maker": "sjtu", "family": "sjtu/limi", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The same agent jobs as LIMI at lower cost. The card says it can trade accuracy on complex tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 684, "dossier_id": "sjtu/openswe/openswe-32b", "name": "OpenSWE-32B", "maker": "sjtu", "family": "sjtu/openswe", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 685, "dossier_id": "sjtu/openswe/openswe-72b", "name": "OpenSWE-72B", "maker": "sjtu", "family": "sjtu/openswe", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 686, "dossier_id": "sk-telecom/a-x/a-x-4-0", "name": "A.X 4.0", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X 4.0 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 687, "dossier_id": "sk-telecom/a-x/a-x-k1", "name": "A.X K1", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X K1 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 688, "dossier_id": "tii/falcon-h1/falcon-h1-1-5b", "name": "Falcon-H1 1.5B", "maker": "tii", "family": "tii/falcon-h1", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 689, "dossier_id": "tii/falcon-h1/falcon-h1-1-5b-deep", "name": "Falcon-H1 1.5B-Deep", "maker": "tii", "family": "tii/falcon-h1", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 690, "dossier_id": "tii/falcon-h1/falcon-h1-3b", "name": "Falcon-H1 3B", "maker": "tii", "family": "tii/falcon-h1", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 691, "dossier_id": "tii/falcon-h1-arabic/falcon-h1-arabic-3b", "name": "Falcon-H1-Arabic 3B", "maker": "tii", "family": "tii/falcon-h1-arabic", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic production model for dialect, STEM, code, and long documents. Three sizes shipped as a family.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 692, "dossier_id": "together/evo/evo-1-131k-base", "name": "Evo-1 131k base", "maker": "together", "family": "together/evo", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Evo-1 131k base (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 693, "dossier_id": "tsinghua/glyph/glyph", "name": "Glyph", "maker": "tsinghua", "family": "tsinghua/glyph", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fit long text into a vision-language context by rendering it as images instead of only extending the text window.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 694, "dossier_id": "tsinghua/minicpm/minicpm5-1b", "name": "MiniCPM5-1B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 695, "dossier_id": "tsinghua/minicpm/minicpm5-2b", "name": "MiniCPM5-2B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A larger on-device MiniCPM5 for local assistants, coding agents, tool use, and reasoning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 696, "dossier_id": "u-tokyo/weblab-medllm/weblab-medllm-gpt-oss-120b", "name": "Weblab-MedLLM-gpt-oss-120b", "maker": "u-tokyo", "family": "u-tokyo/weblab-medllm", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 697, "dossier_id": "ucc/uccix/uccix-mistral-24b", "name": "UCCIX-Mistral-24B", "maker": "ucc", "family": "ucc/uccix", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 698, "dossier_id": "umn/finch/finch-8b", "name": "Finch-8B", "maker": "umn", "family": "umn/finch", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 699, "dossier_id": "umn/finch/finch-8b-kto", "name": "Finch-8B-KTO", "maker": "umn", "family": "umn/finch", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 700, "dossier_id": "umn/scholawrite/scholawrite-llama-3-1-8b-classifier", "name": "ScholaWrite Llama 3.1 8B classifier", "maker": "umn", "family": "umn/scholawrite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 701, "dossier_id": "umn/scholawrite/scholawrite-llama-3-1-8b-writing", "name": "ScholaWrite Llama 3.1 8B writing", "maker": "umn", "family": "umn/scholawrite", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 702, "dossier_id": "umn/scisense/scisense-lm-4g", "name": "SciSense-LM-4G", "maker": "umn", "family": "umn/scisense", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 703, "dossier_id": "writer/palmyra-creative/palmyra-creative", "name": "Palmyra Creative", "maker": "writer", "family": "writer/palmyra-creative", "context_length": 131072, "prompt_price_per_m": 5.0, "completion_price_per_m": 12.0, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 704, "dossier_id": "writer/palmyra-fin/palmyra-fin-70b-32k", "name": "Palmyra-Fin-70B-32K", "maker": "writer", "family": "writer/palmyra-fin", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 705, "dossier_id": "writer/palmyra-mini/palmyra-mini", "name": "Palmyra-mini", "maker": "writer", "family": "writer/palmyra-mini", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 706, "dossier_id": "writer/palmyra-mini/palmyra-mini-thinking-a", "name": "Palmyra-mini-thinking-a", "maker": "writer", "family": "writer/palmyra-mini", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 707, "dossier_id": "writer/palmyra-mini/palmyra-mini-thinking-b", "name": "Palmyra-mini-thinking-b", "maker": "writer", "family": "writer/palmyra-mini", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 708, "dossier_id": "yandex/alice/alice-ai-llm", "name": "Alice AI LLM", "maker": "yandex", "family": "yandex/alice", "context_length": 131072, "prompt_price_per_m": 0.51, "completion_price_per_m": 2.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 709, "dossier_id": "yandex/alice/alice-ai-search-pretrain", "name": "Alice AI Search Pretrain", "maker": "yandex", "family": "yandex/alice", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 710, "dossier_id": "yonsei/spike/spike-scenario-generator", "name": "SPIKE Scenario Generator", "maker": "yonsei", "family": "yonsei/spike", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 711, "dossier_id": "zyphra/zaya/zaya1-8b", "name": "ZAYA1-8B", "maker": "zyphra", "family": "zyphra/zaya", "context_length": 131072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 712, "dossier_id": "sber/gigachat/gigachat-20b-a3b-instruct", "name": "GigaChat-20B-A3B-instruct", "maker": "sber", "family": "sber/gigachat", "context_length": 131000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 713, "dossier_id": "sber/gigachat/gigachat-3-ultra-preview", "name": "GigaChat 3 Ultra Preview", "maker": "sber", "family": "sber/gigachat", "context_length": 131000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 714, "dossier_id": "aisingapore/sea-guard/gemma-sea-guard-12b-2602", "name": "Gemma-SEA-Guard-12B", "maker": "aisingapore", "family": "aisingapore/sea-guard", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 715, "dossier_id": "aisingapore/sea-guard/llama-sea-guard-8b-2602", "name": "Llama-SEA-Guard-8B", "maker": "aisingapore", "family": "aisingapore/sea-guard", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 716, "dossier_id": "aisingapore/sea-guard/qwen-sea-guard-4b-2602", "name": "Qwen-SEA-Guard-4B", "maker": "aisingapore", "family": "aisingapore/sea-guard", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 717, "dossier_id": "aisingapore/sea-guard/qwen-sea-guard-8b-2602", "name": "Qwen-SEA-Guard-8B", "maker": "aisingapore", "family": "aisingapore/sea-guard", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 718, "dossier_id": "aisingapore/sea-lion/gemma-sea-lion-v4-27b", "name": "Gemma-SEA-LION-v4-27B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 719, "dossier_id": "aisingapore/sea-lion/gemma-sea-lion-v4-27b-it", "name": "Gemma-SEA-LION-v4-27B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Southeast Asian instruction following, visual understanding, function calling, and structured output at the 27B Gemma 3 size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 720, "dossier_id": "aisingapore/sea-lion/gemma-sea-lion-v4-27b-vl", "name": "Gemma-SEA-LION-v4-27B-VL", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and text comprehension for Southeast Asian documents and scenes, on top of the 27B instruct model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 721, "dossier_id": "aisingapore/sea-lion/gemma-sea-lion-v4-4b-vl", "name": "Gemma-SEA-LION-v4-4B-VL", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Visual question answering, document extraction, and first-pass tool calling in Southeast Asian languages on a 4B Gemma 3 model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 722, "dossier_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", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Local and agentic tool use in Southeast Asian languages on a small Gemma 4 model, including image and audio inputs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 723, "dossier_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", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger Southeast Asian reasoning model. The free API documents this id.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 724, "dossier_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", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual Southeast Asian reasoning. Toggle thinking_mode off for a direct answer.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 725, "dossier_id": "aisingapore/sea-lion/llama-sea-lion-v3-70b", "name": "Llama-SEA-LION-v3-70B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest SEA-LION v3 base checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 726, "dossier_id": "aisingapore/sea-lion/llama-sea-lion-v3-70b-it", "name": "Llama-SEA-LION-v3-70B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Large instruction model for Southeast Asian languages in the v3 line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 727, "dossier_id": "aisingapore/sea-lion/llama-sea-lion-v3-8b", "name": "Llama-SEA-LION-v3-8B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base 8B checkpoint for SEA-LION v3 before instruction tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 728, "dossier_id": "aisingapore/sea-lion/llama-sea-lion-v3-8b-it", "name": "Llama-SEA-LION-v3-8B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction following in English and Southeast Asian languages at the 8B Llama 3.1 size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 729, "dossier_id": "alibaba/qwen/qwen-max", "name": "Qwen-Max", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 730, "dossier_id": "alibaba/qwen/qwen-turbo", "name": "Qwen-Turbo", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 731, "dossier_id": "alibaba/qwen-embed/qwen3-7-text-embedding", "name": "Qwen3.7-Text-Embedding", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": 128000, "prompt_price_per_m": 0.07, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 732, "dossier_id": "alibaba/qwq/qwq-plus", "name": "QwQ-Plus", "maker": "alibaba", "family": "alibaba/qwq", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hosted QwQ model. A text-generation render lists qwq-plus at 128k with thinking supported and tools unsupported.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 733, "dossier_id": "allenai/olmocr/olmocr-2-7b", "name": "olmOCR 2 7B", "maker": "allenai", "family": "allenai/olmocr", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Current English document OCR model. The October 2025 blog describes unit-test rewards for OCR.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 734, "dossier_id": "cohere/aya/c4ai-aya-expanse-32b", "name": "Aya Expanse 32B", "maker": "cohere", "family": "cohere/aya", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 735, "dossier_id": "cohere/command/command-a-vision-07-2025", "name": "Command A Vision", "maker": "cohere", "family": "cohere/command", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 736, "dossier_id": "cohere/embed/embed-v4-0", "name": "Embed 4", "maker": "cohere", "family": "cohere/embed", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 737, "dossier_id": "cohere/embed/embed-v5-0-fast", "name": "Embed 5 Fast", "maker": "cohere", "family": "cohere/embed", "context_length": 128000, "prompt_price_per_m": 0.08, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 738, "dossier_id": "cohere/embed/embed-v5-0-pro", "name": "Embed 5 Pro", "maker": "cohere", "family": "cohere/embed", "context_length": 128000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 739, "dossier_id": "deepseek/coder/coder-v2", "name": "DeepSeek-Coder-V2", "maker": "deepseek", "family": "deepseek/coder", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 740, "dossier_id": "deepseek/coder/coder-v2-lite", "name": "DeepSeek-Coder-V2-Lite", "maker": "deepseek", "family": "deepseek/coder", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Coder-V2-Lite is the 16B MoE coder released with Coder-V2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 741, "dossier_id": "deepseek/r1/r1-zero", "name": "DeepSeek-R1-Zero", "maker": "deepseek", "family": "deepseek/r1", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 742, "dossier_id": "deepseek/v2/v2", "name": "DeepSeek-V2", "maker": "deepseek", "family": "deepseek/v2", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 743, "dossier_id": "deepseek/v2/v2-5", "name": "DeepSeek-V2.5", "maker": "deepseek", "family": "deepseek/v2", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 744, "dossier_id": "deepseek/v3/v3-2-speciale", "name": "DeepSeek-V3.2-Speciale", "maker": "deepseek", "family": "deepseek/v3", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 745, "dossier_id": "google/gemini/gemini-2-5-computer-use", "name": "Gemini 2.5 Computer Use", "maker": "google", "family": "google/gemini", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google built it for browser automation, multi-step software tasks, and UI testing that need visual reasoning and cursor control.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 746, "dossier_id": "hku/jedi/jedi-3b-1080p", "name": "Jedi-3B-1080p", "maker": "hku", "family": "hku/jedi", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 747, "dossier_id": "hku/jedi/jedi-7b-1080p", "name": "Jedi-7B-1080p", "maker": "hku", "family": "hku/jedi", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 748, "dossier_id": "hku/opencua/opencua-7b", "name": "OpenCUA-7B", "maker": "hku", "family": "hku/opencua", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 749, "dossier_id": "hkust/webexplorer/webexplorer-8b", "name": "WebExplorer-8B", "maker": "hkust", "family": "hkust/webexplorer", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Web agent for long information-seeking tasks. Tools are search and browse.", "summary_intro": "HKUST NLP released WebExplorer-8B, an Apache-2.0 web agent, on 2025-09-08. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 750, "dossier_id": "ibm/granite-code/granite-3b-code-base-128k", "name": "Granite 3B Code Base 128K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 751, "dossier_id": "ibm/granite-code/granite-3b-code-instruct-128k", "name": "Granite 3B Code Instruct 128K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 128000, "prompt_price_per_m": 0.6, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 752, "dossier_id": "ibm/granite-code/granite-8b-code-base-128k", "name": "Granite 8B Code Base 128K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 753, "dossier_id": "ibm/granite-code/granite-8b-code-instruct-128k", "name": "Granite 8B Code Instruct 128K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 754, "dossier_id": "inception/mercury/mercury-voice", "name": "Mercury Voice", "maker": "inception", "family": "inception/mercury", "context_length": 128000, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.75, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 755, "dossier_id": "liquid-ai/lfm2-5/lfm2-5-8b-a1b", "name": "LFM2.5-8B-A1B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 756, "dossier_id": "meituan/flash/longcat-flash-chat", "name": "LongCat-Flash-Chat", "maker": "meituan", "family": "meituan/flash", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 757, "dossier_id": "meta/llama/llama-3-1-405b", "name": "Llama 3.1 405B", "maker": "meta", "family": "meta/llama", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 758, "dossier_id": "meta/llama/llama-3-1-70b", "name": "Llama 3.1 70B", "maker": "meta", "family": "meta/llama", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 759, "dossier_id": "meta/llama/llama-3-2-11b-vision", "name": "Llama 3.2 11B Vision", "maker": "meta", "family": "meta/llama", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 760, "dossier_id": "meta/llama/llama-3-2-90b-vision", "name": "Llama 3.2 90B Vision", "maker": "meta", "family": "meta/llama", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 761, "dossier_id": "microsoft/phi/phi-3-5-mini", "name": "Phi-3.5-mini", "maker": "microsoft", "family": "microsoft/phi", "context_length": 128000, "prompt_price_per_m": 0.00013, "completion_price_per_m": 0.00052, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 762, "dossier_id": "microsoft/phi/phi-3-5-vision", "name": "Phi-3.5-vision", "maker": "microsoft", "family": "microsoft/phi", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 763, "dossier_id": "microsoft/phi/phi-3-medium", "name": "Phi-3-medium", "maker": "microsoft", "family": "microsoft/phi", "context_length": 128000, "prompt_price_per_m": 0.00017, "completion_price_per_m": 0.00068, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 764, "dossier_id": "microsoft/phi/phi-3-mini", "name": "Phi-3-mini", "maker": "microsoft", "family": "microsoft/phi", "context_length": 128000, "prompt_price_per_m": 0.00013, "completion_price_per_m": 0.00052, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 765, "dossier_id": "microsoft/phi/phi-3-small", "name": "Phi-3-small", "maker": "microsoft", "family": "microsoft/phi", "context_length": 128000, "prompt_price_per_m": 0.00015, "completion_price_per_m": 0.0006, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 766, "dossier_id": "microsoft/phi/phi-3-vision", "name": "Phi-3-vision", "maker": "microsoft", "family": "microsoft/phi", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 767, "dossier_id": "microsoft/phi/phi-4-mini-reasoning", "name": "Phi-4-mini-reasoning", "maker": "microsoft", "family": "microsoft/phi", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Microsoft lists it for reasoning-style chat, especially math, at the mini size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 768, "dossier_id": "naver/hyperclova-x/hcx-005", "name": "HCX-005", "maker": "naver", "family": "naver/hyperclova-x", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 769, "dossier_id": "naver/hyperclova-x/hcx-007", "name": "HCX-007", "maker": "naver", "family": "naver/hyperclova-x", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 770, "dossier_id": "naver/hyperclova-x/hyperclova-x-think", "name": "HyperCLOVA X THINK", "maker": "naver", "family": "naver/hyperclova-x", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 771, "dossier_id": "naver/hyperclova-x-seed/seed-think-32b", "name": "HyperCLOVA X SEED Think 32B", "maker": "naver", "family": "naver/hyperclova-x-seed", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 772, "dossier_id": "nec/cotomi/cotomi-v3", "name": "cotomi v3", "maker": "nec", "family": "nec/cotomi", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 773, "dossier_id": "openai/audio/gpt-4o-audio-preview", "name": "GPT-4o Audio", "maker": "openai", "family": "openai/audio", "context_length": 128000, "prompt_price_per_m": 40.0, "completion_price_per_m": 80.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-4o models capable of audio inputs and outputs", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 774, "dossier_id": "openai/audio/gpt-4o-mini-audio-preview", "name": "GPT-4o Mini Audio", "maker": "openai", "family": "openai/audio", "context_length": 128000, "prompt_price_per_m": 10.0, "completion_price_per_m": 20.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller model capable of audio inputs and outputs", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 775, "dossier_id": "openai/audio/gpt-audio-1-5", "name": "GPT-Audio-1.5", "maker": "openai", "family": "openai/audio", "context_length": 128000, "prompt_price_per_m": 32.0, "completion_price_per_m": 64.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The best voice model for audio in, audio out with Chat Completions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 776, "dossier_id": "openai/gpt/chatgpt-4o-latest", "name": "ChatGPT-4o", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 5.0, "completion_price_per_m": 15.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-4o model used in ChatGPT", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 777, "dossier_id": "openai/gpt/gpt-4-5-preview", "name": "GPT-4.5 Preview", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 75.0, "completion_price_per_m": 150.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deprecated large model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 778, "dossier_id": "openai/gpt/gpt-4-turbo-preview", "name": "GPT-4 Turbo Preview", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 10.0, "completion_price_per_m": 30.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An older fast GPT model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 779, "dossier_id": "openai/gpt/gpt-4o-mini-search-preview", "name": "GPT-4o Mini Search Preview", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast, affordable small model for web search", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 780, "dossier_id": "openai/gpt/gpt-4o-search-preview", "name": "GPT-4o Search Preview", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT model for web search in Chat Completions", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 781, "dossier_id": "openai/gpt/gpt-5-1-chat-latest", "name": "GPT-5.1 Chat", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-5.1 model used in ChatGPT", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 782, "dossier_id": "openai/gpt/gpt-5-2-chat-latest", "name": "GPT-5.2 Chat", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 1.75, "completion_price_per_m": 14.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-5.2 model used in ChatGPT", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 783, "dossier_id": "openai/gpt/gpt-5-3-chat-latest", "name": "GPT-5.3 Chat", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 1.75, "completion_price_per_m": 14.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-5.3 Instant model used in ChatGPT", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 784, "dossier_id": "openai/gpt/gpt-5-chat-latest", "name": "GPT-5 Chat", "maker": "openai", "family": "openai/gpt", "context_length": 128000, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GPT-5 model used in ChatGPT", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 785, "dossier_id": "openai/o/o1-preview", "name": "o1 Preview", "maker": "openai", "family": "openai/o", "context_length": 128000, "prompt_price_per_m": 15.0, "completion_price_per_m": 60.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preview of our first o-series reasoning model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 786, "dossier_id": "openai/realtime/gpt-realtime-2", "name": "GPT-Realtime-2", "maker": "openai", "family": "openai/realtime", "context_length": 128000, "prompt_price_per_m": 32.0, "completion_price_per_m": 64.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning model with tool use", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 787, "dossier_id": "openai/realtime/gpt-realtime-2-1", "name": "GPT-Realtime-2.1", "maker": "openai", "family": "openai/realtime", "context_length": 128000, "prompt_price_per_m": 32.0, "completion_price_per_m": 64.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning model with tool use", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 788, "dossier_id": "openai/realtime/gpt-realtime-2-1-mini", "name": "GPT-Realtime-2.1 Mini", "maker": "openai", "family": "openai/realtime", "context_length": 128000, "prompt_price_per_m": 10.0, "completion_price_per_m": 20.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning model with tool use", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 789, "dossier_id": "polyu/unipixel/unipixel-3b", "name": "UniPixel-3B", "maker": "polyu", "family": "polyu/unipixel", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 790, "dossier_id": "polyu/unipixel/unipixel-7b", "name": "UniPixel-7B", "maker": "polyu", "family": "polyu/unipixel", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 791, "dossier_id": "poolside/malibu/malibu-2-2", "name": "Malibu 2.2", "maker": "poolside", "family": "poolside/malibu", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 792, "dossier_id": "rakuten/rakuten-ai-3/rakuten-ai-3-0", "name": "Rakuten AI 3.0", "maker": "rakuten", "family": "rakuten/rakuten-ai-3", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 793, "dossier_id": "ruc/perceval/perceval-3b-policy", "name": "Perceval-3B policy", "maker": "ruc", "family": "ruc/perceval", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 794, "dossier_id": "ruc/perceval/perceval-3b-reward", "name": "Perceval-3B reward", "maker": "ruc", "family": "ruc/perceval", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 795, "dossier_id": "sber/gigachat/gigachat-2", "name": "GigaChat 2 Lite", "maker": "sber", "family": "sber/gigachat", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 796, "dossier_id": "sber/gigachat/gigachat-2-max", "name": "GigaChat 2 Max", "maker": "sber", "family": "sber/gigachat", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 797, "dossier_id": "sber/gigachat/gigachat-2-pro", "name": "GigaChat 2 Pro", "maker": "sber", "family": "sber/gigachat", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 798, "dossier_id": "sber/gigachat/gigachat-3-ultra", "name": "GigaChat 3 Ultra", "maker": "sber", "family": "sber/gigachat", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 799, "dossier_id": "scut/padt/padt-pro-3b", "name": "PaDT-Pro-3B", "maker": "scut", "family": "scut/padt", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 800, "dossier_id": "snowflake/arctic-extract/arctic-extract", "name": "Arctic-Extract", "maker": "snowflake", "family": "snowflake/arctic-extract", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 801, "dossier_id": "snowflake/swiftkv/llama-3-1-swiftkv-405b-instruct", "name": "Llama 3.1 SwiftKV 405B Instruct", "maker": "snowflake", "family": "snowflake/swiftkv", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 802, "dossier_id": "snowflake/swiftkv/llama-3-1-swiftkv-8b-instruct", "name": "Llama 3.1 SwiftKV 8B Instruct", "maker": "snowflake", "family": "snowflake/swiftkv", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 803, "dossier_id": "snowflake/swiftkv/llama-3-3-swiftkv-70b-instruct", "name": "Llama 3.3 SwiftKV 70B Instruct", "maker": "snowflake", "family": "snowflake/swiftkv", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 804, "dossier_id": "softbank/sarashina3/sarashina3-mini", "name": "Sarashina3 mini", "maker": "softbank", "family": "softbank/sarashina3", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 805, "dossier_id": "softbank/sarashina3/sarashina3-nano", "name": "Sarashina3 nano", "maker": "softbank", "family": "softbank/sarashina3", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-volume extraction, tagging, classification, and summarization. The docs say it is for simpler tasks than mini.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 806, "dossier_id": "stockmark/docreasoner/stockmark-docreasoner-qwen2-5-vl-32b", "name": "Stockmark-DocReasoner-Qwen2.5-VL-32B", "maker": "stockmark", "family": "stockmark/docreasoner", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 807, "dossier_id": "u-tokyo/mangalmm/mangalmm", "name": "MangaLMM", "maker": "u-tokyo", "family": "u-tokyo/mangalmm", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 808, "dossier_id": "writer/palmyra-local/palmyra-local-1-7b", "name": "Palmyra-local-1.7B", "maker": "writer", "family": "writer/palmyra-local", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 809, "dossier_id": "writer/palmyra-x/palmyra-x4", "name": "Palmyra X4", "maker": "writer", "family": "writer/palmyra-x", "context_length": 128000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 810, "dossier_id": "xai/grok/grok-1-5", "name": "Grok-1.5", "maker": "xai", "family": "xai/grok", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 811, "dossier_id": "zhipu/codegeex/codegeex-4", "name": "CodeGeeX-4", "maker": "zhipu", "family": "zhipu/codegeex", "context_length": 128000, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CodeGeeX-4 is a codegeex model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 812, "dossier_id": "zhipu/glm/glm-4-32b-0414", "name": "GLM-4-32B-0414", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 813, "dossier_id": "zhipu/glm/glm-4-5-airx", "name": "GLM-4.5-AirX", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 1.1, "completion_price_per_m": 4.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 814, "dossier_id": "zhipu/glm/glm-4-5-flash", "name": "GLM-4.5-Flash", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 815, "dossier_id": "zhipu/glm/glm-4-5-x", "name": "GLM-4.5-X", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 2.2, "completion_price_per_m": 8.9, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 816, "dossier_id": "zhipu/glm/glm-4-air-250414", "name": "GLM-4-Air-250414", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 0.5, "completion_price_per_m": 0.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 817, "dossier_id": "zhipu/glm/glm-4-assistant", "name": "GLM-4-Assistant", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 5.0, "completion_price_per_m": 5.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 818, "dossier_id": "zhipu/glm/glm-4-flash-250414", "name": "GLM-4-Flash-250414", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 819, "dossier_id": "zhipu/glm/glm-4-flashx-250414", "name": "GLM-4-FlashX-250414", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 820, "dossier_id": "zhipu/glm/glm-4-plus", "name": "GLM-4-Plus", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 5.0, "completion_price_per_m": 5.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 821, "dossier_id": "zhipu/glm/glm-z1-air", "name": "GLM-Z1-Air", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 0.5, "completion_price_per_m": 0.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 822, "dossier_id": "zhipu/glm/glm-z1-flash", "name": "GLM-Z1-Flash", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Free reasoning model launched 14 April 2025. Still free on the China price table at 128K, with no cache hit.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 823, "dossier_id": "zhipu/glm/glm-z1-flashx", "name": "GLM-Z1-FlashX", "maker": "zhipu", "family": "zhipu/glm", "context_length": 128000, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 824, "dossier_id": "zhipu/glm-v/glm-4-6v-flash", "name": "GLM-4.6V-Flash", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 128000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 825, "dossier_id": "zhipu/glm-v/glm-4-6v-flashx", "name": "GLM-4.6V-FlashX", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 128000, "prompt_price_per_m": 0.04, "completion_price_per_m": 0.4, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 826, "dossier_id": "zte/nebula-telecom/nebula-telecom", "name": "\u661f\u4e91\u901a\u4fe1\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-telecom", "context_length": 120000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 827, "dossier_id": "anthropic/claude/claude-2", "name": "Claude 2", "maker": "anthropic", "family": "anthropic/claude", "context_length": 100000, "prompt_price_per_m": 8.0, "completion_price_per_m": 24.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 828, "dossier_id": "anthropic/instant/claude-instant-1", "name": "Claude Instant 1.2", "maker": "anthropic", "family": "anthropic/instant", "context_length": 100000, "prompt_price_per_m": 0.8, "completion_price_per_m": 2.4, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 829, "dossier_id": "meta/code-llama/code-llama-13b", "name": "Code Llama 13B", "maker": "meta", "family": "meta/code-llama", "context_length": 100000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 830, "dossier_id": "meta/code-llama/code-llama-34b", "name": "Code Llama 34B", "maker": "meta", "family": "meta/code-llama", "context_length": 100000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 831, "dossier_id": "meta/code-llama/code-llama-7b", "name": "Code Llama 7B", "maker": "meta", "family": "meta/code-llama", "context_length": 100000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 832, "dossier_id": "alibaba/qwen-omni/qwen3-omni-30b-a3b-captioner", "name": "Qwen3-Omni-30B-A3B-Captioner", "maker": "alibaba", "family": "alibaba/qwen-omni", "context_length": 65536, "prompt_price_per_m": 3.81, "completion_price_per_m": 3.06, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 833, "dossier_id": "allenai/bolmo/bolmo-7b", "name": "Bolmo 7B", "maker": "allenai", "family": "allenai/bolmo", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger byte-level Bolmo model with a 65,536-token config window.", "summary_intro": "Bolmo 7B is Ai2's larger December 2025 byte-level language model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 834, "dossier_id": "allenai/olmo-3/olmo-3-1-32b-instruct", "name": "Olmo 3.1 32B Instruct", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Olmo 3.1 32B Instruct is Ai2's December 2025 fully open 32B chat model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 835, "dossier_id": "allenai/olmo-3/olmo-3-1-32b-think", "name": "Olmo 3.1 32B Think", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 836, "dossier_id": "allenai/olmo-3/olmo-3-32b", "name": "Olmo 3 32B", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 837, "dossier_id": "allenai/olmo-3/olmo-3-32b-think", "name": "Olmo 3 32B Think", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 838, "dossier_id": "allenai/olmo-3/olmo-3-7b", "name": "Olmo 3 7B", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 839, "dossier_id": "allenai/olmo-3/olmo-3-7b-instruct", "name": "Olmo 3 7B Instruct", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Efficient instruction model for multi-turn chat and tool use. Final stage is RLVR on the 7B base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 840, "dossier_id": "allenai/olmo-3/olmo-3-7b-rl-zero-code", "name": "Olmo 3 7B RL-Zero Code", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 841, "dossier_id": "allenai/olmo-3/olmo-3-7b-rl-zero-general", "name": "Olmo 3 7B RL-Zero General", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 842, "dossier_id": "allenai/olmo-3/olmo-3-7b-rl-zero-if", "name": "Olmo 3 7B RL-Zero IF", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 843, "dossier_id": "allenai/olmo-3/olmo-3-7b-rl-zero-math", "name": "Olmo 3 7B RL-Zero Math", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 844, "dossier_id": "allenai/olmo-3/olmo-3-7b-rl-zero-mix", "name": "Olmo 3 7B RL-Zero Mix", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 845, "dossier_id": "allenai/olmo-3/olmo-3-7b-think", "name": "Olmo 3 7B Think", "maker": "allenai", "family": "allenai/olmo-3", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B reasoning model that surfaces intermediate thinking steps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 846, "dossier_id": "allenai/olmo-hybrid/olmo-hybrid-7b", "name": "Olmo Hybrid 7B", "maker": "allenai", "family": "allenai/olmo-hybrid", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 847, "dossier_id": "allenai/olmo-hybrid/olmo-hybrid-7b-instruct", "name": "Olmo Hybrid 7B Instruct", "maker": "allenai", "family": "allenai/olmo-hybrid", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned Olmo Hybrid 7B. The latest public stage found is the DPO checkpoint; no final RL name was found.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 848, "dossier_id": "allenai/olmo-hybrid/olmo-hybrid-7b-think", "name": "Olmo Hybrid 7B Think", "maker": "allenai", "family": "allenai/olmo-hybrid", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning SFT checkpoint of Olmo Hybrid 7B. Only the Think SFT stage was found.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 849, "dossier_id": "arcee/afm/afm-4-5b", "name": "AFM-4.5B", "maker": "arcee", "family": "arcee/afm", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Enterprise chat, tool use, and edge or CPU deployment where a small dense model is enough.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 850, "dossier_id": "arcee/afm/afm-4-5b-base", "name": "AFM-4.5B-Base", "maker": "arcee", "family": "arcee/afm", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Continued training and fine-tuning, not direct chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 851, "dossier_id": "baichuan/baichuan-audio/baichuan-audio", "name": "Baichuan-Audio", "maker": "baichuan", "family": "baichuan/baichuan-audio", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 852, "dossier_id": "baichuan/baichuan-audio/baichuan-audio-base", "name": "Baichuan-Audio-Base", "maker": "baichuan", "family": "baichuan/baichuan-audio", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 853, "dossier_id": "baichuan/baichuan-omni/baichuan-omni-1-5", "name": "Baichuan-Omni-1.5", "maker": "baichuan", "family": "baichuan/baichuan-omni", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 854, "dossier_id": "databricks/mpt/mpt-7b-storywriter", "name": "MPT-7B-StoryWriter-65k+", "maker": "databricks", "family": "databricks/mpt", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-context story reading and writing. MosaicML released it to show ALiBi extrapolation, with a 65k training context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 855, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 856, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 857, "dossier_id": "epfl/apertus/apertus-70b", "name": "Apertus 70B", "maker": "epfl", "family": "epfl/apertus", "context_length": 65536, "prompt_price_per_m": 0.82, "completion_price_per_m": 2.92, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 858, "dossier_id": "epfl/apertus/apertus-8b", "name": "Apertus 8B", "maker": "epfl", "family": "epfl/apertus", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 859, "dossier_id": "eth/apertus/apertus-70b", "name": "Apertus 70B", "maker": "eth", "family": "eth/apertus", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 70B (apertus-70b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 860, "dossier_id": "eth/apertus/apertus-70b-instruct", "name": "Apertus 70B Instruct", "maker": "eth", "family": "eth/apertus", "context_length": 65536, "prompt_price_per_m": 0.82, "completion_price_per_m": 2.92, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 70B Instruct (apertus-70b-instruct). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 861, "dossier_id": "eth/apertus/apertus-8b", "name": "Apertus 8B", "maker": "eth", "family": "eth/apertus", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 8B (apertus-8b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 862, "dossier_id": "eth/apertus/apertus-8b-instruct", "name": "Apertus 8B Instruct", "maker": "eth", "family": "eth/apertus", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 8B Instruct (apertus-8b-instruct). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 863, "dossier_id": "geneva/aladdin/aladdin-3b", "name": "Aladdin-3B", "maker": "geneva", "family": "geneva/aladdin", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 864, "dossier_id": "google/gemini-image/gemini-2-5-flash-image", "name": "Nano Banana", "maker": "google", "family": "google/gemini-image", "context_length": 65536, "prompt_price_per_m": 0.3, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes it as the fast native image generation and editing model for high-volume creative workflows.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 865, "dossier_id": "google/gemini-image/gemini-3-1-flash-lite-image", "name": "Nano Banana 2 Lite", "maker": "google", "family": "google/gemini-image", "context_length": 65536, "prompt_price_per_m": 0.25, "completion_price_per_m": 1.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes it as the efficiency specialist for high-volume interactive image generation and editing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 866, "dossier_id": "huggingface/smollm/smollm3-3b", "name": "SmolLM3-3B", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face's fully open 3B instruction model with dual-mode reasoning and tool calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 867, "dossier_id": "huggingface/smollm/smollm3-3b-base", "name": "SmolLM3-3B-Base", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face's fully open 3B pretrained base for the SmolLM3 3B line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 868, "dossier_id": "huggingface/zephyr/zephyr-orpo-141b", "name": "Zephyr ORPO 141B", "maker": "huggingface", "family": "huggingface/zephyr", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The largest Zephyr assistant, an ORPO fine-tune of Mixtral-8x22B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 869, "dossier_id": "lg/exaone/exaone-4-0-1-2b", "name": "EXAONE 4.0 1.2B", "maker": "lg", "family": "lg/exaone", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "EXAONE 4.0 1.2B LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 870, "dossier_id": "lightblue/karasu/karasu-mixtral-8x22b-v0-1", "name": "Karasu-Mixtral-8x22B-v0.1", "maker": "lightblue", "family": "lightblue/karasu", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 871, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "LLM-jp-4.1 post-train of the 32B-A3B checkpoint, with tool calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 872, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "LLM-jp-4.1 post-train of the 33B checkpoint, including tool calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 873, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 874, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MoE base: pre-training and mid-training only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 875, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT and DPO thinking model, no RL. Active parameters stay about 3.83B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 876, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Dense 33.2B base after pre-training and mid-training. Intended as a base for further tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 877, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 878, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base checkpoint: pre-training and mid-training only, no SFT or DPO.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 879, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 880, "dossier_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", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Post-trained with SFT and DPO, without reinforcement learning. Reasoning effort is exposed in the chat template.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 881, "dossier_id": "nous-research/yarn/yarn-llama-2-13b-64k", "name": "Yarn Llama 2 13B 64k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 882, "dossier_id": "nous-research/yarn/yarn-llama-2-7b-64k", "name": "Yarn Llama 2 7B 64k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 883, "dossier_id": "nous-research/yarn/yarn-mistral-7b-64k", "name": "Yarn Mistral 7B 64k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 884, "dossier_id": "nous-research/yarn/yarn-solar-10b-64k", "name": "Yarn SOLAR 10.7B 64k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 885, "dossier_id": "princeton/prolong/llama-3-8b-prolong-64k-base", "name": "ProLong-64k-Base", "maker": "princeton", "family": "princeton/prolong", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 886, "dossier_id": "princeton/prolong/llama-3-8b-prolong-64k-instruct", "name": "ProLong-64k-Instruct", "maker": "princeton", "family": "princeton/prolong", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 887, "dossier_id": "sjtu/torl/torl-1-5b", "name": "ToRL-1.5B", "maker": "sjtu", "family": "sjtu/torl", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 888, "dossier_id": "stepfun/step/step-3", "name": "Step 3", "maker": "stepfun", "family": "stepfun/step", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 889, "dossier_id": "tsinghua/minicpm/minicpm4-1-8b", "name": "MiniCPM4.1-8B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MiniCPM4.1 adds hybrid reasoning and a faster reasoning decode path on the MiniCPM4 sparse-attention design.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 890, "dossier_id": "upstage/solar/solar-pro-2", "name": "Solar Pro 2", "maker": "upstage", "family": "upstage/solar", "context_length": 65536, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Upstage positions it for enterprise reasoning, tool use, and Korean, English, and Japanese chat.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 891, "dossier_id": "upstage/syn/syn-pro", "name": "Syn Pro", "maker": "upstage", "family": "upstage/syn", "context_length": 65536, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese business chat. The catalog says it does not add tool calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 892, "dossier_id": "yandex/alice/alice-ai-llm-flash", "name": "Alice AI LLM Flash", "maker": "yandex", "family": "yandex/alice", "context_length": 65536, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.2, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Moderation, classification, support bots, summarization, file and knowledge-base search, and other latency-sensitive agent steps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 893, "dossier_id": "bar-ilan/dictalm/dictalm-3-0-24b", "name": "DictaLM 3.0 24B", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 65280, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "24B base model. The card says it is not a chat model. The blog calls 24B the flagship.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 894, "dossier_id": "bar-ilan/dictalm/dictalm-3-0-24b-thinking", "name": "DictaLM 3.0 24B Thinking", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 65280, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning chat model with tool calling. The release page prints Hebrew chat scores for this checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 895, "dossier_id": "microsoft/phi/phi-4-mini-flash-reasoning", "name": "Phi-4-mini-flash-reasoning", "maker": "microsoft", "family": "microsoft/phi", "context_length": 64000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Microsoft presents it as a faster small reasoning model for math and other reasoning-dense prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 896, "dossier_id": "microsoft/vibevoice/vibevoice-1-5b", "name": "VibeVoice-1.5B", "maker": "microsoft", "family": "microsoft/vibevoice", "context_length": 64000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-form conversational speech from text, not a short single-utterance reader.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 897, "dossier_id": "microsoft/vibevoice/vibevoice-asr", "name": "VibeVoice-ASR", "maker": "microsoft", "family": "microsoft/vibevoice", "context_length": 64000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Transcription with diarization and timestamps, including custom hotwords and code-switching, without setting a language.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 898, "dossier_id": "minimax/m/m2-her", "name": "M2-her", "maker": "minimax", "family": "minimax/m", "context_length": 64000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Role-play and multi-turn character conversation, according to the MiniMax text guide.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 899, "dossier_id": "softbank/sarashina3/sarashina3-guard", "name": "Sarashina3 guard", "maker": "softbank", "family": "softbank/sarashina3", "context_length": 64000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 900, "dossier_id": "zhipu/glm-v/glm-4-1v-thinking-flash", "name": "GLM-4.1V-Thinking-Flash", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 64000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 901, "dossier_id": "zhipu/glm-v/glm-4-1v-thinking-flashx", "name": "GLM-4.1V-Thinking-FlashX", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 64000, "prompt_price_per_m": 2.0, "completion_price_per_m": 2.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 902, "dossier_id": "bar-ilan/dictalm/dictalm-3-0-1-7b-instruct", "name": "DictaLM 3.0 1.7B Instruct", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 62080, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.7B instruct model with tool calling. The card shows a chat template.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 903, "dossier_id": "bar-ilan/dictalm/dictalm-3-0-1-7b-thinking", "name": "DictaLM 3.0 1.7B Thinking", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 62080, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.7B reasoning chat model with tool calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 904, "dossier_id": "baichuan/baichuan-m/baichuan-m3-235b", "name": "Baichuan-M3-235B", "maker": "baichuan", "family": "baichuan/baichuan-m", "context_length": 40960, "prompt_price_per_m": 0.01, "completion_price_per_m": 0.03, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 905, "dossier_id": "ecnu/educhat/educhat-r1-001-32b-qwen3-0", "name": "EduChat-R1 32B (Qwen3)", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 906, "dossier_id": "ecnu/educhat/educhat-r1-001-8b-qwen3-0", "name": "EduChat-R1 8B (Qwen3)", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 907, "dossier_id": "ecnu/psychagent/psychagent-qwen3-32b", "name": "PsychAgent-Qwen3-32B", "maker": "ecnu", "family": "ecnu/psychagent", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 908, "dossier_id": "neu/gram/gram-qwen3-1-7b", "name": "GRAM-Qwen3-1.7B", "maker": "neu", "family": "neu/gram", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 909, "dossier_id": "neu/gram/gram-qwen3-14b", "name": "GRAM-Qwen3-14B", "maker": "neu", "family": "neu/gram", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 910, "dossier_id": "neu/gram/gram-qwen3-4b", "name": "GRAM-Qwen3-4B", "maker": "neu", "family": "neu/gram", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 911, "dossier_id": "neu/gram/gram-qwen3-8b", "name": "GRAM-Qwen3-8B", "maker": "neu", "family": "neu/gram", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 912, "dossier_id": "neu/techgpt/techgpt-3-0", "name": "TechGPT-3.0", "maker": "neu", "family": "neu/techgpt", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 913, "dossier_id": "njust/hssallm/hssallm-32b", "name": "HssaLLM-32B", "maker": "njust", "family": "njust/hssallm", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 914, "dossier_id": "njust/hssallm/hssallm-32b-base", "name": "HssaLLM-32B-Base", "maker": "njust", "family": "njust/hssallm", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 915, "dossier_id": "njust/hssallm/hssallm-8b", "name": "HssaLLM-8B", "maker": "njust", "family": "njust/hssallm", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 916, "dossier_id": "prime-intellect/intellect/intellect-2", "name": "INTELLECT-2", "maker": "prime-intellect", "family": "prime-intellect/intellect", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 917, "dossier_id": "ruc/clawgym/clawgym-8b", "name": "ClawGym-8B", "maker": "ruc", "family": "ruc/clawgym", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 918, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-8b", "name": "InternVL3.5-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 919, "dossier_id": "sustech/calibsft/qwen3-8b-calibsft", "name": "Qwen3-8B-CalibSFT", "maker": "sustech", "family": "sustech/calibsft", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 920, "dossier_id": "sustech/calibsft/qwen3-8b-calibsft-rlcr", "name": "Qwen3-8B-CalibSFT-RLCR", "maker": "sustech", "family": "sustech/calibsft", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 921, "dossier_id": "tongji/med-go/med-go-32b", "name": "Med-Go-32B", "maker": "tongji", "family": "tongji/med-go", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 922, "dossier_id": "tsinghua/minicpm-o/minicpm-o-4-5", "name": "MiniCPM-o 4.5", "maker": "tsinghua", "family": "tsinghua/minicpm-o", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full-duplex live interaction: take video and audio while generating speech and text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 923, "dossier_id": "tsinghua/minicpm-v/minicpm-v-4-5", "name": "MiniCPM-V 4.5", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-image, multi-image, and high-frame-rate video understanding, aimed at phone-class deployment.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 924, "dossier_id": "uark/qupaint/qupaint-4b", "name": "QuPAINT-4B", "maker": "uark", "family": "uark/qupaint", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 925, "dossier_id": "umd/dynaguard/dynaguard-1-7b", "name": "DynaGuard-1.7B", "maker": "umd", "family": "umd/dynaguard", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 926, "dossier_id": "umd/dynaguard/dynaguard-4b", "name": "DynaGuard-4B", "maker": "umd", "family": "umd/dynaguard", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 927, "dossier_id": "umd/dynaguard/dynaguard-8b", "name": "DynaGuard-8B", "maker": "umd", "family": "umd/dynaguard", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 928, "dossier_id": "umn/scisense/scisense-lm-30", "name": "SciSense-LM-30", "maker": "umn", "family": "umn/scisense", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 929, "dossier_id": "umn/scisense/scisense-lm-32", "name": "SciSense-LM-32", "maker": "umn", "family": "umn/scisense", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 930, "dossier_id": "umn/scisense/scisense-lm-4q", "name": "SciSense-LM-4Q", "maker": "umn", "family": "umn/scisense", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 931, "dossier_id": "umn/scisense/scisense-lm-4qi", "name": "SciSense-LM-4QI", "maker": "umn", "family": "umn/scisense", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 932, "dossier_id": "zte/nebula-telecom/ntele-8b", "name": "NTele-8B", "maker": "zte", "family": "zte/nebula-telecom", "context_length": 40960, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 933, "dossier_id": "cyberagent/cat/cat-paws-8b", "name": "CAT-Paws-8B", "maker": "cyberagent", "family": "cyberagent/cat", "context_length": 40000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 934, "dossier_id": "allenai/molmo2/molmo2-8b", "name": "Molmo 2 8B", "maker": "allenai", "family": "allenai/molmo2", "context_length": 36864, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Molmo 2 8B is Ai2's December 2025 flagship open video-language model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 935, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "32K-context base variant of Yi-1.5-34B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 936, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "32K-context base variant of Yi-1.5-9B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 937, "dossier_id": "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese", "name": "Mixtral-8x7B-Instruct-v0.1-japanese", "maker": "abeja", "family": "abeja/mixtral", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 938, "dossier_id": "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese-202408", "name": "Mixtral-8x7B-Instruct-v0.1-japanese-202408", "maker": "abeja", "family": "abeja/mixtral", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 939, "dossier_id": "abeja/mixtral/mixtral-8x7b-instruct-v0-1-japanese-alpha", "name": "Mixtral-8x7B-Instruct-v0.1-japanese-alpha", "maker": "abeja", "family": "abeja/mixtral", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 940, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 941, "dossier_id": "abeja/mixtral/mixtral-8x7b-v0-1-japanese", "name": "Mixtral-8x7B-v0.1-japanese", "maker": "abeja", "family": "abeja/mixtral", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 942, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 943, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 944, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 945, "dossier_id": "abeja/qwq/abeja-qwq32b-reasoning-japanese-v1-0", "name": "ABEJA-QwQ32b-Reasoning-Japanese-v1.0", "maker": "abeja", "family": "abeja/qwq", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 946, "dossier_id": "agh/bielik/bielik-11b-v2", "name": "Bielik-11B-v2", "maker": "agh", "family": "agh/bielik", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 947, "dossier_id": "agh/bielik/bielik-11b-v2-instruct", "name": "Bielik-11B-v2-Instruct", "maker": "agh", "family": "agh/bielik", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 948, "dossier_id": "agh/bielik/bielik-11b-v3-dflash", "name": "Bielik-11B-v3.0-DFlash", "maker": "agh", "family": "agh/bielik", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 949, "dossier_id": "agh/bielik/bielik-4-5b-v3", "name": "Bielik-4.5B-v3", "maker": "agh", "family": "agh/bielik", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Compact Polish base model. The card points chat users to Bielik-4.5B-v3.0-Instruct.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 950, "dossier_id": "agh/bielik/bielik-4-5b-v3-instruct", "name": "Bielik-4.5B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction following in Polish at the 4.5B scale, including a math-oriented GRPO stage described in the report.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 951, "dossier_id": "agh/bielik/bielik-minitron-7b-v3-dflash", "name": "Bielik-Minitron-7B-v3.0-DFlash", "maker": "agh", "family": "agh/bielik", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speculative draft for the Minitron instruct target. Same serving pattern as the 11B DFlash card (SGLang and vLLM).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 952, "dossier_id": "aisingapore/sea-lion/qwen-sea-lion-v4-32b-it", "name": "Qwen-SEA-LION-v4-32B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning and instruction following across Southeast Asian languages, with a thinking mode switch. Text in, text out.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 953, "dossier_id": "alibaba/qwen/qwen2-5-3b", "name": "Qwen2.5-3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "3B Qwen2.5 instruct model. The China column of the Qwen2.5 API table lists a 32,768 context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 954, "dossier_id": "alibaba/qwen-omni/qwen2-5-omni-7b", "name": "Qwen2.5-Omni-7B", "maker": "alibaba", "family": "alibaba/qwen-omni", "context_length": 32768, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.4, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 955, "dossier_id": "alibaba/qwq/qwq-32b-preview", "name": "QwQ-32B-Preview", "maker": "alibaba", "family": "alibaba/qwq", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preview reasoning model released before QwQ-32B. The card warns of limitations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 956, "dossier_id": "allenai/sera/sera-32b", "name": "SERA 32B", "maker": "allenai", "family": "allenai/sera", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Repository coding agent trained with soft-verified trajectories.", "summary_intro": "SERA 32B is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 957, "dossier_id": "allenai/sera/sera-32b-ga", "name": "SERA 32B GA", "maker": "allenai", "family": "allenai/sera", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Repository coding agent trained with soft-verified trajectories.", "summary_intro": "SERA 32B GA is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 958, "dossier_id": "allenai/sera/sera-8b", "name": "SERA 8B", "maker": "allenai", "family": "allenai/sera", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Repository coding agent trained with soft-verified trajectories.", "summary_intro": "SERA 8B is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 959, "dossier_id": "allenai/sera/sera-8b-ga", "name": "SERA 8B GA", "maker": "allenai", "family": "allenai/sera", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Repository coding agent trained with soft-verified trajectories.", "summary_intro": "SERA 8B GA is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 960, "dossier_id": "amazon/titan/titan-text-premier", "name": "Amazon Titan Text G1 - Premier", "maker": "amazon", "family": "amazon/titan", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-ended and contextual answers, code generation, summarization, and Bedrock Knowledge Bases and Agents. Custom fine-tuning was marked preview.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 961, "dossier_id": "apple/afm/afm-3-cloud", "name": "AFM 3 Cloud", "maker": "apple", "family": "apple/afm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 962, "dossier_id": "apple/fastvlm/fastvlm-0-5b", "name": "FastVLM-0.5B", "maker": "apple", "family": "apple/fastvlm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device visual question answering. Apple positions FastViTHD as a hybrid vision encoder that cuts encoding latency at high resolution.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 963, "dossier_id": "apple/fastvlm/fastvlm-1-5b", "name": "FastVLM-1.5B", "maker": "apple", "family": "apple/fastvlm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device visual question answering. Apple positions FastViTHD as a hybrid vision encoder that cuts encoding latency at high resolution.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 964, "dossier_id": "apple/fastvlm/fastvlm-7b", "name": "FastVLM-7B", "maker": "apple", "family": "apple/fastvlm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device visual question answering. Apple positions FastViTHD as a hybrid vision encoder that cuts encoding latency at high resolution.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 965, "dossier_id": "arcee/blitz/arcee-blitz", "name": "Arcee-Blitz", "maker": "arcee", "family": "arcee/blitz", "context_length": 32768, "prompt_price_per_m": 0.45, "completion_price_per_m": 0.75, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast general chat and document tasks without a larger model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 966, "dossier_id": "arcee/blitz/blitz-caller-beta", "name": "Arcee Blitz Caller Beta", "maker": "arcee", "family": "arcee/blitz", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-turn function calling and tool selection.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 967, "dossier_id": "arcee/caller/caller", "name": "Caller", "maker": "arcee", "family": "arcee/caller", "context_length": 32768, "prompt_price_per_m": 0.55, "completion_price_per_m": 0.85, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Function calling and multi-system orchestration.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 968, "dossier_id": "arcee/glm/glm-4-32b-base-32k", "name": "GLM-4-32B-Base-32K", "maker": "arcee", "family": "arcee/glm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-context continued pretraining research and downstream fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 969, "dossier_id": "arcee/lite/arcee-lite", "name": "Arcee-Lite", "maker": "arcee", "family": "arcee/lite", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A small demonstration of Arcee's distillation stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 970, "dossier_id": "arcee/nova/arcee-nova", "name": "Arcee-Nova", "maker": "arcee", "family": "arcee/nova", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General reasoning, writing, and coding at the Qwen2-72B scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 971, "dossier_id": "arcee/raspberry/raspberry-3b", "name": "raspberry-3B", "maker": "arcee", "family": "arcee/raspberry", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning experiments, not production chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 972, "dossier_id": "arcee/scribe/arcee-scribe", "name": "Arcee-Scribe", "maker": "arcee", "family": "arcee/scribe", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Creative writing and dialogue.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 973, "dossier_id": "arcee/spark/arcee-spark", "name": "Arcee-Spark", "maker": "arcee", "family": "arcee/spark", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Conversational AI in the 6B to 9B range.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 974, "dossier_id": "arcee/virtuoso/virtuoso-lite", "name": "Virtuoso-Lite", "maker": "arcee", "family": "arcee/virtuoso", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A small distillation for reasoning, code, and math at 10B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 975, "dossier_id": "arcee/vylinh/arcee-vylinh", "name": "Arcee-VyLinh", "maker": "arcee", "family": "arcee/vylinh", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vietnamese chat and instruction following, with English input also accepted.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 976, "dossier_id": "baai/bge/bge-code-v1", "name": "bge-code-v1", "maker": "baai", "family": "baai/bge", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Natural-language to code retrieval, plus multilingual text retrieval at a similar scale to text embedders of this size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 977, "dossier_id": "baai/bge/bge-en-icl", "name": "bge-en-icl", "maker": "baai", "family": "baai/bge", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 978, "dossier_id": "baai/emu/emu3-5", "name": "Emu3.5", "maker": "baai", "family": "baai/emu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 979, "dossier_id": "baichuan/baichuan-m/baichuan-m2-plus", "name": "Baichuan-M2-Plus", "maker": "baichuan", "family": "baichuan/baichuan-m", "context_length": 32768, "prompt_price_per_m": 0.01, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 980, "dossier_id": "baichuan/baichuan-m/baichuan-m3-plus", "name": "Baichuan-M3-Plus", "maker": "baichuan", "family": "baichuan/baichuan-m", "context_length": 32768, "prompt_price_per_m": 0.005, "completion_price_per_m": 0.009, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 981, "dossier_id": "baichuan/baichuan2/baichuan2-53b", "name": "Baichuan2-53B", "maker": "baichuan", "family": "baichuan/baichuan2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 982, "dossier_id": "baichuan/baichuan2/baichuan2-turbo", "name": "Baichuan2-Turbo", "maker": "baichuan", "family": "baichuan/baichuan2", "context_length": 32768, "prompt_price_per_m": 0.008, "completion_price_per_m": 0.008, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 983, "dossier_id": "baichuan/baichuan3/baichuan3-turbo", "name": "Baichuan3-Turbo", "maker": "baichuan", "family": "baichuan/baichuan3", "context_length": 32768, "prompt_price_per_m": 0.012, "completion_price_per_m": 0.012, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 984, "dossier_id": "baichuan/baichuan4/baichuan4", "name": "Baichuan 4", "maker": "baichuan", "family": "baichuan/baichuan4", "context_length": 32768, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.1, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 985, "dossier_id": "baichuan/baichuan4/baichuan4-air", "name": "Baichuan4-Air", "maker": "baichuan", "family": "baichuan/baichuan4", "context_length": 32768, "prompt_price_per_m": 0.00098, "completion_price_per_m": 0.00098, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 986, "dossier_id": "baichuan/baichuan4/baichuan4-turbo", "name": "Baichuan4-Turbo", "maker": "baichuan", "family": "baichuan/baichuan4", "context_length": 32768, "prompt_price_per_m": 0.015, "completion_price_per_m": 0.015, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 987, "dossier_id": "bar-ilan/dictalm/dictalm-2-0", "name": "DictaLM 2.0", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is a pretrained generative model specialized for Hebrew text, for further fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 988, "dossier_id": "bar-ilan/dictalm/dictalm-2-0-instruct", "name": "DictaLM 2.0 Instruct", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hebrew and English instruction following. The card says it was tuned on conversation datasets.", "summary_intro": "DictaLM 2.0 Instruct is the public chat fine-tune of DictaLM 2.0. Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 989, "dossier_id": "bar-ilan/dictalm/dictalm-3-0-1-7b", "name": "DictaLM 3.0 1.7B", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.7B base model for edge hardware. The card says it is a base model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 990, "dossier_id": "bnu/muduo/muduo-llm", "name": "\u5e08\u627f\u4e07\u8c61\u57fa\u7840\u6559\u80b2\u5927\u6a21\u578b\uff08MuduoLLM\uff09", "maker": "bnu", "family": "bnu/muduo", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 991, "dossier_id": "case/ease/qwen2-5-1-5b-instruct-ease", "name": "Qwen2.5-1.5B-Instruct-EASE", "maker": "case", "family": "case/ease", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 992, "dossier_id": "case/ease/qwen2-5-3b-instruct-ease", "name": "Qwen2.5-3B-Instruct-EASE", "maker": "case", "family": "case/ease", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 993, "dossier_id": "cohere/rerank/rerank-v4-0-fast", "name": "Rerank 4 Fast", "maker": "cohere", "family": "cohere/rerank", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The faster Rerank 4 model. Same 32k multilingual reranking job as Pro, aimed at high throughput.", "summary_intro": "The faster Rerank 4 model. Same 32k multilingual reranking job as Pro, aimed at high throughput. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 994, "dossier_id": "cohere/rerank/rerank-v4-0-pro", "name": "Rerank 4 Pro", "maker": "cohere", "family": "cohere/rerank", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 995, "dossier_id": "cyberagent/calm/calm2-7b-chat", "name": "CyberAgentLM2-7B-Chat", "maker": "cyberagent", "family": "cyberagent/calm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 996, "dossier_id": "databricks/dbrx/dbrx-base", "name": "DBRX Base", "maker": "databricks", "family": "databricks/dbrx", "context_length": 32768, "prompt_price_per_m": 10.714, "completion_price_per_m": 32.143, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 997, "dossier_id": "databricks/dbrx/dbrx-instruct", "name": "DBRX Instruct", "maker": "databricks", "family": "databricks/dbrx", "context_length": 32768, "prompt_price_per_m": 10.714, "completion_price_per_m": 32.143, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 998, "dossier_id": "ecnu/educhat/educhat-sft-002-1-8b-qwen1-5", "name": "EduChat 1.0 1.8B (Qwen1.5)", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 999, "dossier_id": "ecnu/educhat/educhat-sft-002-32b-qwen1-5", "name": "EduChat 1.0 32B (Qwen1.5)", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1000, "dossier_id": "ecnu/educhat/educhat-sft-003-32b-qwen2-5", "name": "EduChat 2.0 32B (Qwen2.5)", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1001, "dossier_id": "ecnu/educhat/educhat-sft-003-7b-qwen2-5", "name": "EduChat 2.0 7B (Qwen2.5)", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1002, "dossier_id": "ecnu/sea/sea-e", "name": "SEA-E", "maker": "ecnu", "family": "ecnu/sea", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1003, "dossier_id": "ecnu/sea/sea-s", "name": "SEA-S", "maker": "ecnu", "family": "ecnu/sea", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1004, "dossier_id": "edinburgh/eurollm/eurollm-22b", "name": "EuroLLM-22B", "maker": "edinburgh", "family": "edinburgh/eurollm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1005, "dossier_id": "edinburgh/eurollm/eurollm-22b-instruct", "name": "EuroLLM-22B-Instruct", "maker": "edinburgh", "family": "edinburgh/eurollm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1006, "dossier_id": "edinburgh/eurovlm/eurovlm-1-7b-preview", "name": "EuroVLM-1.7B-Preview", "maker": "edinburgh", "family": "edinburgh/eurovlm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1007, "dossier_id": "edinburgh/eurovlm/eurovlm-9b-preview", "name": "EuroVLM-9B-Preview", "maker": "edinburgh", "family": "edinburgh/eurovlm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1008, "dossier_id": "elyza/shortcut/elyza-shortcut-1-0-qwen-32b", "name": "ELYZA-Shortcut-1.0-Qwen-32B", "maker": "elyza", "family": "elyza/shortcut", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1009, "dossier_id": "elyza/shortcut/elyza-shortcut-1-0-qwen-7b", "name": "ELYZA-Shortcut-1.0-Qwen-7B", "maker": "elyza", "family": "elyza/shortcut", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1010, "dossier_id": "essential-ai/eai-distill/eai-distill-0-5b", "name": "EAI-Distill-0.5B", "maker": "essential-ai", "family": "essential-ai/eai-distill", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1011, "dossier_id": "essential-ai/rnj/rnj-1", "name": "Rnj-1", "maker": "essential-ai", "family": "essential-ai/rnj", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1012, "dossier_id": "essential-ai/rnj/rnj-1-instruct", "name": "Rnj-1 Instruct", "maker": "essential-ai", "family": "essential-ai/rnj", "context_length": 32768, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.15, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1013, "dossier_id": "ghent/tweety/tweety-7b-armenian", "name": "Tweety-7B Armenian", "maker": "ghent", "family": "ghent/tweety", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1014, "dossier_id": "ghent/tweety/tweety-7b-italian", "name": "Tweety-7B Italian", "maker": "ghent", "family": "ghent/tweety", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1015, "dossier_id": "ghent/tweety/tweety-7b-tatar", "name": "Tweety-7B Tatar", "maker": "ghent", "family": "ghent/tweety", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1016, "dossier_id": "google/gemini/gemini-1-0-pro", "name": "Gemini 1.0 Pro", "maker": "google", "family": "google/gemini", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1017, "dossier_id": "google/gemini/gemini-1-0-ultra", "name": "Gemini 1.0 Ultra", "maker": "google", "family": "google/gemini", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The technical report defines Ultra for highly complex tasks, above Pro and above on-device Nano.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1018, "dossier_id": "google/gemma/gemma-3-1b", "name": "Gemma 3 1B", "maker": "google", "family": "google/gemma", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1019, "dossier_id": "google/gemma/gemma-3-270m", "name": "Gemma 3 270M", "maker": "google", "family": "google/gemma", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation and, on the 4B, 12B, and 27B sizes, image understanding.", "summary_intro": "Google DeepMind open-weights Gemma 3 model. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1020, "dossier_id": "google/gemma/gemma-3n-e2b", "name": "Gemma 3n E2B", "maker": "google", "family": "google/gemma", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Google DeepMind on-device multimodal Gemma 3n model. Collected 2026-10-15. Open weights, active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1021, "dossier_id": "google/gemma/gemma-3n-e4b", "name": "Gemma 3n E4B", "maker": "google", "family": "google/gemma", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Google DeepMind on-device multimodal Gemma 3n model. Collected 2026-10-15. Open weights, active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1022, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1023, "dossier_id": "hit/huozi/huozi-3", "name": "\u6d3b\u5b57 3.0", "maker": "hit", "family": "hit/huozi", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1024, "dossier_id": "hit/huozi/huozi-3-5", "name": "\u6d3b\u5b57 3.5", "maker": "hit", "family": "hit/huozi", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1025, "dossier_id": "hit/kalm/kalm-embedding-multilingual-mini-instruct-v2", "name": "KaLM-embedding-multilingual-mini-instruct-v2", "maker": "hit", "family": "hit/kalm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1026, "dossier_id": "hit/kalm/kalm-embedding-multilingual-mini-instruct-v2-5", "name": "KaLM-embedding-multilingual-mini-instruct-v2.5", "maker": "hit", "family": "hit/kalm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1027, "dossier_id": "hku/agenttrek/agenttrek-1-0-32b", "name": "AgentTrek-1.0-32B", "maker": "hku", "family": "hku/agenttrek", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1028, "dossier_id": "hku/aguvis/aguvis-7b-720p", "name": "Aguvis-7B-720P", "maker": "hku", "family": "hku/aguvis", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1029, "dossier_id": "hku/videoagenttrek/videoagenttrek-cua-7b", "name": "VideoAgentTrek-CUA-7B", "maker": "hku", "family": "hku/videoagenttrek", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1030, "dossier_id": "hku/videoagenttrek/videoagenttrek-idm-s1-7b", "name": "VideoAgentTrek-IDM-s1-7B", "maker": "hku", "family": "hku/videoagenttrek", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1031, "dossier_id": "hku/videoagenttrek/videoagenttrek-idm-s2-7b", "name": "VideoAgentTrek-IDM-s2-7B", "maker": "hku", "family": "hku/videoagenttrek", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1032, "dossier_id": "hu-berlin/news-crawler/fundus-crawler-json", "name": "FundusCrawler JSON", "maker": "hu-berlin", "family": "hu-berlin/news-crawler", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper presents a second output format that emits article fields as JSON. This folder is the JSON Hub repo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1033, "dossier_id": "hu-berlin/news-crawler/fundus-crawler-plaintext", "name": "FundusCrawler plaintext", "maker": "hu-berlin", "family": "hu-berlin/news-crawler", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1034, "dossier_id": "huawei/openpangu/openpangu-embedded-1b", "name": "openPangu-Embedded-1B", "maker": "huawei", "family": "huawei/openpangu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device fast-thinking language model trained from scratch on Ascend.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1035, "dossier_id": "huawei/openpangu/openpangu-embedded-7b", "name": "openPangu-Embedded-7B", "maker": "huawei", "family": "huawei/openpangu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B dense model with fused fast and slow thinking, trained on Ascend.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1036, "dossier_id": "huawei/pangu-industry/pangu-medical-nlp-n2-32k", "name": "Pangu-Medical-NLP-N2-32K", "maker": "huawei", "family": "huawei/pangu-industry", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical NLP for reasoning, information extraction, and text generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1037, "dossier_id": "huawei/pangu-industry/pangu-medical-nlp-n2-reasoner-32k", "name": "Pangu-Medical-NLP-N2-Reasoner-32K", "maker": "huawei", "family": "huawei/pangu-industry", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slow-thinking medical NLP at 32K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1038, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n1-32k", "name": "Pangu-NLP-N1-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast-thinking Chinese NLP model for fine-tuning and inference at a 32K sequence length on ModelArts Studio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1039, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n2-32k", "name": "Pangu-NLP-N2-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "N2 NLP line at 32K, including fused fast and slow thinking on the newest build.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1040, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n2-reasoner-32k", "name": "Pangu-NLP-N2-Reasoner-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slow-thinking N2 reasoner at 32K, with pretraining, LoRA, and GRPO marked.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1041, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n4-32k", "name": "Pangu-NLP-N4-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "N4 NLP model documented for 32K inference deployment.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1042, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n4-reasoner-32k", "name": "Pangu-NLP-N4-Reasoner-32K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slow-thinking N4 reasoner for 32K training and inference.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1043, "dossier_id": "huggingface/idefics2/idefics2-8b", "name": "Idefics2-8B", "maker": "huggingface", "family": "huggingface/idefics2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The main Idefics2-8B instruction model for images and text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1044, "dossier_id": "huggingface/idefics2/idefics2-8b-base", "name": "Idefics2-8B-Base", "maker": "huggingface", "family": "huggingface/idefics2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base Idefics2-8B checkpoint for further multimodal training.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1045, "dossier_id": "huggingface/idefics2/idefics2-8b-chatty", "name": "Idefics2-8B-Chatty", "maker": "huggingface", "family": "huggingface/idefics2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Longer-conversation fine-tune of Idefics2-8B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1046, "dossier_id": "huggingface/olympiccoder/olympiccoder-32b", "name": "OlympicCoder-32B", "maker": "huggingface", "family": "huggingface/olympiccoder", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Open R1 32B competitive-coding reasoner.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1047, "dossier_id": "huggingface/olympiccoder/olympiccoder-7b", "name": "OlympicCoder-7B", "maker": "huggingface", "family": "huggingface/olympiccoder", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Open R1 7B competitive-coding reasoner.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1048, "dossier_id": "huggingface/open-r1/openr1-distill-7b", "name": "OpenR1-Distill-7B", "maker": "huggingface", "family": "huggingface/open-r1", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face's open reproduction of a DeepSeek-R1 distilled 7B reasoner.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1049, "dossier_id": "huggingface/open-r1/openr1-qwen-7b", "name": "OpenR1-Qwen-7B", "maker": "huggingface", "family": "huggingface/open-r1", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Early Hugging Face Open R1 7B math SFT checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1050, "dossier_id": "huggingface/zephyr/zephyr-7b-alpha", "name": "Zephyr 7B Alpha", "maker": "huggingface", "family": "huggingface/zephyr", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First Zephyr assistant, a DPO fine-tune of Mistral-7B-v0.1 by the Hugging Face H4 team.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1051, "dossier_id": "huggingface/zephyr/zephyr-7b-beta", "name": "Zephyr 7B Beta", "maker": "huggingface", "family": "huggingface/zephyr", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The main Zephyr 7B assistant from the 2023 technical report, tuned with distilled DPO.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1052, "dossier_id": "huji/slam/slam", "name": "Slam", "maker": "huji", "family": "huji/slam", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1053, "dossier_id": "huji/slam/slam-scaled", "name": "Slam Scaled", "maker": "huji", "family": "huji/slam", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1054, "dossier_id": "ibm/granite/granite-4-0-350m", "name": "Granite-4.0-350M", "maker": "ibm", "family": "ibm/granite", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1055, "dossier_id": "ibm/granite/granite-4-0-350m-base", "name": "Granite-4.0-350M-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1056, "dossier_id": "ibm/granite/granite-4-0-h-350m", "name": "Granite-4.0-H-350M", "maker": "ibm", "family": "ibm/granite", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1057, "dossier_id": "ibm/granite/granite-4-0-h-350m-base", "name": "Granite-4.0-H-350M-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1058, "dossier_id": "ibm/granite-embedding/granite-embedding-311m-multilingual-r2", "name": "Granite-Embedding-311M-Multilingual-R2", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1059, "dossier_id": "ibm/granite-embedding/granite-embedding-97m-multilingual-r2", "name": "Granite-Embedding-97M-Multilingual-R2", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1060, "dossier_id": "iflytek/spark/spark-4-0-ultra", "name": "Spark 4.0 Ultra", "maker": "iflytek", "family": "iflytek/spark", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1061, "dossier_id": "iflytek/spark/spark-max-32k", "name": "Spark Max-32K", "maker": "iflytek", "family": "iflytek/spark", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1062, "dossier_id": "iitb/param/param2-17b-a2-4b-thinking", "name": "Param-2 17B A2.4B Thinking", "maker": "iitb", "family": "iitb/param", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1063, "dossier_id": "inception/mercury/mercury-coder-mini", "name": "Mercury Coder Mini", "maker": "inception", "family": "inception/mercury", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1064, "dossier_id": "inception/mercury/mercury-coder-small", "name": "Mercury Coder Small", "maker": "inception", "family": "inception/mercury", "context_length": 32768, "prompt_price_per_m": 0.25, "completion_price_per_m": 1.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1065, "dossier_id": "innsbruck/argus/argus-colqwen3-5-2b", "name": "Argus-ColQwen3.5-2B", "maker": "innsbruck", "family": "innsbruck/argus", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1066, "dossier_id": "innsbruck/argus/argus-colqwen3-5-4b", "name": "Argus-ColQwen3.5-4B", "maker": "innsbruck", "family": "innsbruck/argus", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1067, "dossier_id": "innsbruck/argus/argus-colqwen3-5-9b", "name": "Argus-ColQwen3.5-9B", "maker": "innsbruck", "family": "innsbruck/argus", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1068, "dossier_id": "jhu/followir/followir-7b", "name": "FollowIR-7B", "maker": "jhu", "family": "jhu/followir", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1069, "dossier_id": "jina/code-embeddings/jina-code-embeddings-0-5b", "name": "jina-code-embeddings-0.5b", "maker": "jina", "family": "jina/code-embeddings", "context_length": 32768, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller code embedding sibling of jina-code-embeddings-1.5b.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1070, "dossier_id": "jina/code-embeddings/jina-code-embeddings-1-5b", "name": "jina-code-embeddings-1.5b", "maker": "jina", "family": "jina/code-embeddings", "context_length": 32768, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code retrieval across natural language, code, comments, and completion, in 15+ programming languages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1071, "dossier_id": "jina/embeddings/jina-embeddings-v4", "name": "jina-embeddings-v4", "maker": "jina", "family": "jina/embeddings", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1072, "dossier_id": "jina/embeddings/jina-embeddings-v5-omni-small", "name": "jina-embeddings-v5-omni-small", "maker": "jina", "family": "jina/embeddings", "context_length": 32768, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One embedding space for text, images, audio, video, and PDFs, with text vectors that match the v5 text-small model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1073, "dossier_id": "jina/embeddings/jina-embeddings-v5-text-small", "name": "jina-embeddings-v5-text-small", "maker": "jina", "family": "jina/embeddings", "context_length": 32768, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1074, "dossier_id": "jina/reader/jina-ocr-v1", "name": "jina-ocr-v1", "maker": "jina", "family": "jina/reader", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Page-image and PDF reader that emits Markdown, including layout-heavy documents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1075, "dossier_id": "jina/reader/jina-vlm", "name": "jina-vlm", "maker": "jina", "family": "jina/reader", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small multilingual vision-language model. Jina lists it with readers because it reads images to text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1076, "dossier_id": "kakao/kanana/kanana-1-5-15-7b-a3b-base", "name": "Kanana 1.5 15.7B-A3B Base", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Kakao says the sparse model matches the dense 1.5 8B class at about 37% of the FLOPs per token.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1077, "dossier_id": "kakao/kanana/kanana-1-5-15-7b-a3b-instruct", "name": "Kanana 1.5 15.7B-A3B Instruct", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following and tool use at a lower FLOP cost than the dense 8B, according to Kakao.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1078, "dossier_id": "kakao/kanana/kanana-1-5-2-1b-base", "name": "Kanana 1.5 2.1B Base", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base weights for the 1.5 small model, with a longer native context than Kanana Nano.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1079, "dossier_id": "kakao/kanana/kanana-1-5-2-1b-instruct-2505", "name": "Kanana 1.5 2.1B Instruct 2505", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1080, "dossier_id": "kakao/kanana/kanana-1-5-8b-base", "name": "Kanana 1.5 8B Base", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base model for the public 8B 1.5 line. Kakao compares it with the closed original Kanana 8B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1081, "dossier_id": "kakao/kanana/kanana-1-5-8b-instruct-2505", "name": "Kanana 1.5 8B Instruct 2505", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1082, "dossier_id": "kakao/kanana/kanana-2-1-3b-base", "name": "Kanana-2 1.3B Base", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1083, "dossier_id": "kakao/kanana/kanana-2-1-3b-instruct", "name": "Kanana-2 1.3B Instruct", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1084, "dossier_id": "kakao/kanana/kanana-2-30b-a3b-base", "name": "Kanana-2 30B-A3B Base", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1085, "dossier_id": "kakao/kanana/kanana-2-30b-a3b-base-2601", "name": "Kanana-2 30B-A3B Base 2601", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1086, "dossier_id": "kakao/kanana/kanana-2-30b-a3b-instruct", "name": "Kanana-2 30B-A3B Instruct", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1087, "dossier_id": "kakao/kanana/kanana-2-30b-a3b-instruct-2601", "name": "Kanana-2 30B-A3B Instruct 2601", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1088, "dossier_id": "kakao/kanana/kanana-2-30b-a3b-mid-2601", "name": "Kanana-2 30B-A3B Mid 2601", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1089, "dossier_id": "kakao/kanana/kanana-2-30b-a3b-thinking", "name": "Kanana-2 30B-A3B Thinking", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1090, "dossier_id": "kakao/kanana/kanana-2-30b-a3b-thinking-2601", "name": "Kanana-2 30B-A3B Thinking 2601", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1091, "dossier_id": "kakao/kanana/kanana-2-3b-base", "name": "Kanana-2 3B Base", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1092, "dossier_id": "kakao/kanana/kanana-2-3b-instruct", "name": "Kanana-2 3B Instruct", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1093, "dossier_id": "kakao/kanana/kanana-essence-1-5-9-8b", "name": "Kanana Essence 1.5 9.8B", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1094, "dossier_id": "kakao/kanana/kanana-flag-1-5-32-5b", "name": "Kanana Flag 1.5 32.5B", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Kakao uses it as the top 1.5 comparison model for coding, math, and function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1095, "dossier_id": "kakao/kanana/kanana-nano-1-5-3b", "name": "Kanana Nano 1.5 3B", "maker": "kakao", "family": "kakao/kanana", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1096, "dossier_id": "kakao/kanana-v/kanana-1-5-v-3b-instruct", "name": "Kanana-1.5-V 3B Instruct", "maker": "kakao", "family": "kakao/kanana-v", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Kakao says it is for image captioning, document understanding, OCR reasoning, and bilingual multimodal instruction following.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1097, "dossier_id": "kentucky/kos/kos-v5-instruct", "name": "KOS-V5-Instruct", "maker": "kentucky", "family": "kentucky/kos", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1098, "dossier_id": "korea-university/renderrank/renderrank-2b", "name": "RenderRank-2B", "maker": "korea-university", "family": "korea-university/renderrank", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1099, "dossier_id": "lg/exaone/exaone-3-5-2-4b-instruct", "name": "EXAONE 3.5 2.4B Instruct", "maker": "lg", "family": "lg/exaone", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned bilingual model for real-world instructions and long context, in an on-device, general, or larger size.", "summary_intro": "EXAONE 3.5 2.4B Instruct LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1100, "dossier_id": "lg/exaone/exaone-3-5-32b-instruct", "name": "EXAONE 3.5 32B Instruct", "maker": "lg", "family": "lg/exaone", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned bilingual model for real-world instructions and long context, in an on-device, general, or larger size.", "summary_intro": "EXAONE 3.5 32B Instruct LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1101, "dossier_id": "lg/exaone/exaone-3-5-7-8b-instruct", "name": "EXAONE 3.5 7.8B Instruct", "maker": "lg", "family": "lg/exaone", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned bilingual model for real-world instructions and long context, in an on-device, general, or larger size.", "summary_intro": "EXAONE 3.5 7.8B Instruct LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1102, "dossier_id": "lg/exaone-deep/exaone-deep-2-4b", "name": "EXAONE Deep 2.4B", "maker": "lg", "family": "lg/exaone-deep", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning model for math, science, and code, built on the matching EXAONE 3.5 Instruct checkpoint.", "summary_intro": "EXAONE Deep 2.4B LG AI Research released this as exaone-deep. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1103, "dossier_id": "lg/exaone-deep/exaone-deep-32b", "name": "EXAONE Deep 32B", "maker": "lg", "family": "lg/exaone-deep", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning model for math, science, and code, built on the matching EXAONE 3.5 Instruct checkpoint.", "summary_intro": "EXAONE Deep 32B LG AI Research released this as exaone-deep. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1104, "dossier_id": "lg/exaone-deep/exaone-deep-7-8b", "name": "EXAONE Deep 7.8B", "maker": "lg", "family": "lg/exaone-deep", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning model for math, science, and code, built on the matching EXAONE 3.5 Instruct checkpoint.", "summary_intro": "EXAONE Deep 7.8B LG AI Research released this as exaone-deep. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1105, "dossier_id": "lightblue/ao-karasu/ao-karasu-72b", "name": "ao-Karasu 72B", "maker": "lightblue", "family": "lightblue/ao-karasu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1106, "dossier_id": "lightblue/jod/jod", "name": "JOD", "maker": "lightblue", "family": "lightblue/jod", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese chatbot. The card prefers ChatML because the SlimOrca base used it.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1107, "dossier_id": "lightblue/karasu/karasu-7b", "name": "Karasu 7B", "maker": "lightblue", "family": "lightblue/karasu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1108, "dossier_id": "lightblue/karasu/karasu-7b-chat", "name": "Karasu 7B Chat", "maker": "lightblue", "family": "lightblue/karasu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1109, "dossier_id": "lightblue/karasu/karasu-7b-chat-plus", "name": "Karasu 7B Chat Plus", "maker": "lightblue", "family": "lightblue/karasu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1110, "dossier_id": "lightblue/karasu/karasu-7b-chat-plus-unleashed", "name": "Karasu 7B Chat Plus Unleashed", "maker": "lightblue", "family": "lightblue/karasu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1111, "dossier_id": "lightblue/karasu/karasu-dpo-7b", "name": "Karasu-DPO-7B", "maker": "lightblue", "family": "lightblue/karasu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1112, "dossier_id": "lightblue/kurage/kurage-ar", "name": "Kurage Arabic", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1113, "dossier_id": "lightblue/kurage/kurage-en", "name": "Kurage English", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1114, "dossier_id": "lightblue/kurage/kurage-es", "name": "Kurage Spanish", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1115, "dossier_id": "lightblue/kurage/kurage-hi", "name": "Kurage Hindi", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1116, "dossier_id": "lightblue/kurage/kurage-id", "name": "Kurage Indonesian", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1117, "dossier_id": "lightblue/kurage/kurage-ja", "name": "Kurage Japanese", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1118, "dossier_id": "lightblue/kurage/kurage-ko", "name": "Kurage Korean", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1119, "dossier_id": "lightblue/kurage/kurage-multilingual", "name": "Kurage Multilingual", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1120, "dossier_id": "lightblue/kurage/kurage-ru", "name": "Kurage Russian", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1121, "dossier_id": "lightblue/kurage/kurage-sw", "name": "Kurage Swahili", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1122, "dossier_id": "lightblue/kurage/kurage-th", "name": "Kurage Thai", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1123, "dossier_id": "lightblue/kurage/kurage-vi", "name": "Kurage Vietnamese", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1124, "dossier_id": "lightblue/kurage/kurage-zh", "name": "Kurage Chinese", "maker": "lightblue", "family": "lightblue/kurage", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1125, "dossier_id": "lightblue/lb-reranker/lb-reranker-0-5b-v1-0", "name": "LB Reranker 0.5B v1.0", "maker": "lightblue", "family": "lightblue/lb-reranker", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1126, "dossier_id": "lighton/alfred/alfred-ft5", "name": "Alfred Frontier 5", "maker": "lighton", "family": "lighton/alfred", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1127, "dossier_id": "lighton/alfred/alfred-sv5", "name": "Alfred Sovereign 5", "maker": "lighton", "family": "lighton/alfred", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1128, "dossier_id": "lighton/qwen3-reason/qwen3-8b-de", "name": "Qwen3-8B-DE", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1129, "dossier_id": "lighton/qwen3-reason/qwen3-8b-de-pivot-en", "name": "Qwen3-8B-DE-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1130, "dossier_id": "lighton/qwen3-reason/qwen3-8b-de-swap", "name": "Qwen3-8B-DE-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1131, "dossier_id": "lighton/qwen3-reason/qwen3-8b-en", "name": "Qwen3-8B-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1132, "dossier_id": "lighton/qwen3-reason/qwen3-8b-es", "name": "Qwen3-8B-ES", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1133, "dossier_id": "lighton/qwen3-reason/qwen3-8b-es-pivot-en", "name": "Qwen3-8B-ES-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1134, "dossier_id": "lighton/qwen3-reason/qwen3-8b-es-swap", "name": "Qwen3-8B-ES-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1135, "dossier_id": "lighton/qwen3-reason/qwen3-8b-fr", "name": "Qwen3-8B-FR", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1136, "dossier_id": "lighton/qwen3-reason/qwen3-8b-fr-pivot-en", "name": "Qwen3-8B-FR-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1137, "dossier_id": "lighton/qwen3-reason/qwen3-8b-fr-swap", "name": "Qwen3-8B-FR-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1138, "dossier_id": "lighton/qwen3-reason/qwen3-8b-sw", "name": "Qwen3-8B-SW", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1139, "dossier_id": "lighton/qwen3-reason/qwen3-8b-sw-pivot-en", "name": "Qwen3-8B-SW-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1140, "dossier_id": "lighton/qwen3-reason/qwen3-8b-sw-swap", "name": "Qwen3-8B-SW-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1141, "dossier_id": "lighton/qwen3-reason/qwen3-8b-zh", "name": "Qwen3-8B-ZH", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1142, "dossier_id": "lighton/qwen3-reason/qwen3-8b-zh-pivot-en", "name": "Qwen3-8B-ZH-Pivot-EN", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1143, "dossier_id": "lighton/qwen3-reason/qwen3-8b-zh-swap", "name": "Qwen3-8B-ZH-Swap", "maker": "lighton", "family": "lighton/qwen3-reason", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1144, "dossier_id": "linz/bio-xlstm/dna-xlstm-4m", "name": "DNA-xLSTM-4M", "maker": "linz", "family": "linz/bio-xlstm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1145, "dossier_id": "liquid-ai/d1/d1-3b", "name": "d1-3B", "maker": "liquid-ai", "family": "liquid-ai/d1", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1146, "dossier_id": "liquid-ai/lfm2/lfm2-1-2b", "name": "LFM2-1.2B", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1147, "dossier_id": "liquid-ai/lfm2/lfm2-2-6b", "name": "LFM2-2.6B", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1148, "dossier_id": "liquid-ai/lfm2/lfm2-2-6b-exp", "name": "LFM2-2.6B-Exp", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1149, "dossier_id": "liquid-ai/lfm2/lfm2-24b-a2b", "name": "LFM2-24B-A2B", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1150, "dossier_id": "liquid-ai/lfm2/lfm2-350m", "name": "LFM2-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1151, "dossier_id": "liquid-ai/lfm2/lfm2-700m", "name": "LFM2-700M", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1152, "dossier_id": "liquid-ai/lfm2/lfm2-8b-a1b", "name": "LFM2-8B-A1B", "maker": "liquid-ai", "family": "liquid-ai/lfm2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1153, "dossier_id": "liquid-ai/lfm2-5/lfm2-5-1-2b-instruct", "name": "LFM2.5-1.2B-Instruct", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1154, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1155, "dossier_id": "liquid-ai/lfm2-5/lfm2-5-1-2b-thinking", "name": "LFM2.5-1.2B-Thinking", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1156, "dossier_id": "liquid-ai/lfm2-5/lfm2-5-230m", "name": "LFM2.5-230M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest LFM2.5 chat model, for data extraction and lightweight on-device agents. Blog date 25 June 2026.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1157, "dossier_id": "liquid-ai/lfm2-5/lfm2-5-350m", "name": "LFM2.5-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1158, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1159, "dossier_id": "liquid-ai/lfm2-5-vl/lfm2-5-vl-3b", "name": "LFM2.5-VL-3B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-vl", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1160, "dossier_id": "liquid-ai/lfm2-5-vl/lfm2-5-vl-450m", "name": "LFM2.5-VL-450M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-vl", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Compact vision model for structured outputs on the edge, including bounding boxes. Released 8 April 2026.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1161, "dossier_id": "liquid-ai/lfm2-nano/lfm2-1-2b-longevity", "name": "LFM2-1.2B-Longevity", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1162, "dossier_id": "liquid-ai/lfm2-nano/lfm2-2-6b-longevity", "name": "LFM2-2.6B-Longevity", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1163, "dossier_id": "liquid-ai/lfm2-vl/lfm2-vl-1-6b", "name": "LFM2-VL-1.6B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-vl", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1164, "dossier_id": "liquid-ai/lfm2-vl/lfm2-vl-3b", "name": "LFM2-VL-3B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-vl", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1165, "dossier_id": "liquid-ai/lfm2-vl/lfm2-vl-450m", "name": "LFM2-VL-450M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-vl", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1166, "dossier_id": "ljubljana/gams/gams2-27b", "name": "GaMS2-27B", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not stated on the card. The translator card uses it as the base for an English-to-Slovene specialist.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1167, "dossier_id": "ljubljana/gams/gams2-27b-grpo-translator", "name": "GaMS2-27B-GRPO-Translator", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-to-Slovene translation with a fixed translator prompt. The card says not to add commentary.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1168, "dossier_id": "ljubljana/gams/gams2-27b-instruct", "name": "GaMS2-27B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not stated on the card. The arena lists it as a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1169, "dossier_id": "microsoft/phi/phi-4-reasoning", "name": "Phi-4-reasoning", "maker": "microsoft", "family": "microsoft/phi", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Microsoft presents it for complex reasoning in math, science, and code, using a long reasoning trace before the answer.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1170, "dossier_id": "microsoft/phi/phi-4-reasoning-plus", "name": "Phi-4-reasoning-plus", "maker": "microsoft", "family": "microsoft/phi", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same reasoning jobs as Phi-4-reasoning when a longer trace is acceptable.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1171, "dossier_id": "mit/vila/nvila", "name": "NVILA", "maker": "mit", "family": "mit/vila", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1172, "dossier_id": "moonshot/moonshot-v1/moonshot-v1-32k", "name": "Moonshot v1 32K", "maker": "moonshot", "family": "moonshot/moonshot-v1", "context_length": 32768, "prompt_price_per_m": 5.0, "completion_price_per_m": 20.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Longer text. Same model, larger context cap.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1173, "dossier_id": "moonshot/moonshot-v1/moonshot-v1-32k-vision-preview", "name": "Moonshot v1 32K Vision", "maker": "moonshot", "family": "moonshot/moonshot-v1", "context_length": 32768, "prompt_price_per_m": 5.0, "completion_price_per_m": 20.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision preview at the 32K cap.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1174, "dossier_id": "naver/hyperclova-x/hcx-l", "name": "HCX-L", "maker": "naver", "family": "naver/hyperclova-x", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1175, "dossier_id": "naver/hyperclova-x/hcx-s", "name": "HCX-S", "maker": "naver", "family": "naver/hyperclova-x", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The report calls it the lightweight alternative, still aimed at Korean and English instruction following.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1176, "dossier_id": "neu/lmt/lmt-60-0-6b", "name": "LMT-60-0.6B", "maker": "neu", "family": "neu/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1177, "dossier_id": "neu/lmt/lmt-60-1-7b", "name": "LMT-60-1.7B", "maker": "neu", "family": "neu/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1178, "dossier_id": "neu/lmt/lmt-60-4b", "name": "LMT-60-4B", "maker": "neu", "family": "neu/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1179, "dossier_id": "neu/lmt/lmt-60-8b", "name": "LMT-60-8B", "maker": "neu", "family": "neu/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1180, "dossier_id": "neu/techgpt/techgpt-2-0-qwen1-5", "name": "TechGPT-2.0-Qwen1.5", "maker": "neu", "family": "neu/techgpt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1181, "dossier_id": "niutrans/lmt/lmt-60-0-6b", "name": "LMT-60-0.6B", "maker": "niutrans", "family": "niutrans/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1182, "dossier_id": "niutrans/lmt/lmt-60-0-6b-base", "name": "LMT-60-0.6B-Base", "maker": "niutrans", "family": "niutrans/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1183, "dossier_id": "niutrans/lmt/lmt-60-1-7b", "name": "LMT-60-1.7B", "maker": "niutrans", "family": "niutrans/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1184, "dossier_id": "niutrans/lmt/lmt-60-1-7b-base", "name": "LMT-60-1.7B-Base", "maker": "niutrans", "family": "niutrans/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1185, "dossier_id": "niutrans/lmt/lmt-60-4b", "name": "LMT-60-4B", "maker": "niutrans", "family": "niutrans/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1186, "dossier_id": "niutrans/lmt/lmt-60-4b-base", "name": "LMT-60-4B-Base", "maker": "niutrans", "family": "niutrans/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1187, "dossier_id": "niutrans/lmt/lmt-60-8b", "name": "LMT-60-8B", "maker": "niutrans", "family": "niutrans/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1188, "dossier_id": "niutrans/lmt/lmt-60-8b-base", "name": "LMT-60-8B-Base", "maker": "niutrans", "family": "niutrans/lmt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1189, "dossier_id": "njust/hssallm/hssallm-8b-base", "name": "HssaLLM-8B-Base", "maker": "njust", "family": "njust/hssallm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1190, "dossier_id": "northeastern/moxin/moxin-7b-chat", "name": "Moxin 7B Chat", "maker": "northeastern", "family": "northeastern/moxin", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1191, "dossier_id": "northeastern/moxin/moxin-7b-instruct", "name": "Moxin 7B Instruct", "maker": "northeastern", "family": "northeastern/moxin", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1192, "dossier_id": "northeastern/moxin/moxin-7b-llm", "name": "Moxin 7B", "maker": "northeastern", "family": "northeastern/moxin", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1193, "dossier_id": "nous-research/yarn/yarn-llama-2-70b-32k", "name": "Yarn Llama 2 70B 32k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1194, "dossier_id": "nous-research/yarn/yarn-solar-10b-32k", "name": "Yarn SOLAR 10.7B 32k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1195, "dossier_id": "ntu-taiwan/lawstral/taiwan-lawstral", "name": "Taiwan-Lawstral", "maker": "ntu-taiwan", "family": "ntu-taiwan/lawstral", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1196, "dossier_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", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1197, "dossier_id": "ntu-taiwan/taiwan-llm/taiwan-llm-8x7b-dpo", "name": "Taiwan LLM 8x7B DPO", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1198, "dossier_id": "nyu/cambrian-s/cambrian-s-7b", "name": "Cambrian-S 7B", "maker": "nyu", "family": "nyu/cambrian-s", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1199, "dossier_id": "oslo/normistral/normistral-11b-long", "name": "NorMistral-11B-long", "maker": "oslo", "family": "oslo/normistral", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1200, "dossier_id": "peking/g1/g1-3b", "name": "G1-3B", "maker": "peking", "family": "peking/g1", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "G1-3B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1201, "dossier_id": "peking/g1/g1-7b", "name": "G1-7B", "maker": "peking", "family": "peking/g1", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "G1-7B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1202, "dossier_id": "peking/g1/g1-zero-7b", "name": "G1-Zero-7B", "maker": "peking", "family": "peking/g1", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "G1-Zero-7B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1203, "dossier_id": "polyu/videomind/videomind-2b", "name": "VideoMind-2B", "maker": "polyu", "family": "polyu/videomind", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1204, "dossier_id": "polyu/videomind/videomind-7b", "name": "VideoMind-7B", "maker": "polyu", "family": "polyu/videomind", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1205, "dossier_id": "preferred-networks/plamo-2/plamo-2-1-2b-cpt", "name": "PLaMo 2.1 2B CPT", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1206, "dossier_id": "preferred-networks/plamo-2/plamo-2-1-8b-cpt", "name": "PLaMo 2.1 8B CPT", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1207, "dossier_id": "preferred-networks/plamo-2/plamo-2-2-prime", "name": "PLaMo 2.2 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1208, "dossier_id": "princeton/swe-agent-lm/swe-agent-lm-32b", "name": "SWE-agent-LM-32B", "maker": "princeton", "family": "princeton/swe-agent-lm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1209, "dossier_id": "rakuten/rakuten-ai/rakuten-ai-7b", "name": "Rakuten AI 7B", "maker": "rakuten", "family": "rakuten/rakuten-ai", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1210, "dossier_id": "rakuten/rakuten-ai/rakuten-ai-7b-chat", "name": "Rakuten AI 7B Chat", "maker": "rakuten", "family": "rakuten/rakuten-ai", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1211, "dossier_id": "rakuten/rakuten-ai/rakuten-ai-7b-instruct", "name": "Rakuten AI 7B Instruct", "maker": "rakuten", "family": "rakuten/rakuten-ai", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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:`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1212, "dossier_id": "rakuten/rakuten-ai-2/rakuten-ai-2-0-8x7b", "name": "Rakuten AI 2.0", "maker": "rakuten", "family": "rakuten/rakuten-ai-2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1213, "dossier_id": "rakuten/rakuten-ai-2/rakuten-ai-2-0-8x7b-instruct", "name": "Rakuten AI 2.0 Instruct", "maker": "rakuten", "family": "rakuten/rakuten-ai-2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1214, "dossier_id": "rinna/bakeneko/qwen2-5-bakeneko-32b-instruct", "name": "Qwen2.5 Bakeneko 32B Instruct", "maker": "rinna", "family": "rinna/bakeneko", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction model of Qwen2.5 Bakeneko 32B, made with a chat vector and SimPO.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1215, "dossier_id": "rinna/nekomata/nekomata-7b", "name": "Nekomata 7B", "maker": "rinna", "family": "rinna/nekomata", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pretrain of Qwen-7B on about 30B tokens. Nekomata is the youkai \u732b\u53c8.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1216, "dossier_id": "rinna/nekomata/nekomata-7b-instruction", "name": "Nekomata 7B Instruction", "maker": "rinna", "family": "rinna/nekomata", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction tune of Nekomata 7B in the Alpaca format.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1217, "dossier_id": "ruc/cir/qwen-math-7b-cir", "name": "Qwen-Math-7B-CIR", "maker": "ruc", "family": "ruc/cir", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1218, "dossier_id": "ruc/simpledeepsearcher/simpledeepsearcher-qwen-32b", "name": "SimpleDeepSearcher Qwen-32B", "maker": "ruc", "family": "ruc/simpledeepsearcher", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1219, "dossier_id": "ruc/simpledeepsearcher/simpledeepsearcher-qwen-7b", "name": "SimpleDeepSearcher Qwen-7B", "maker": "ruc", "family": "ruc/simpledeepsearcher", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1220, "dossier_id": "ruc/still/still-2", "name": "STILL-2", "maker": "ruc", "family": "ruc/still", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1221, "dossier_id": "ruc/swe-master/swe-master-32b-lsp", "name": "SWE-Master-32B-LSP", "maker": "ruc", "family": "ruc/swe-master", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1222, "dossier_id": "ruc/swe-master/swe-master-32b-rl", "name": "SWE-Master-32B-RL", "maker": "ruc", "family": "ruc/swe-master", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1223, "dossier_id": "ruc/swe-master/swe-master-32b-sft", "name": "SWE-Master-32B-SFT", "maker": "ruc", "family": "ruc/swe-master", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1224, "dossier_id": "ruc/swe-world/swe-world-32b-rl", "name": "SWE-World-32B-RL", "maker": "ruc", "family": "ruc/swe-world", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1225, "dossier_id": "ruc/swe-world/swe-world-32b-sft", "name": "SWE-World-32B-SFT", "maker": "ruc", "family": "ruc/swe-world", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1226, "dossier_id": "ruc/virgo/virgo-72b", "name": "Virgo-72B", "maker": "ruc", "family": "ruc/virgo", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1227, "dossier_id": "sakana/tinyswallow/tinyswallow-1-5b", "name": "TinySwallow-1.5B", "maker": "sakana", "family": "sakana/tinyswallow", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Compact Japanese base LM for research and further tuning. Not the instruction model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1228, "dossier_id": "sakana/tinyswallow/tinyswallow-1-5b-instruct", "name": "TinySwallow-1.5B-Instruct", "maker": "sakana", "family": "sakana/tinyswallow", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Japanese instruction model that can run locally or in the browser.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1229, "dossier_id": "salesforce/sfr/sfr-embedding", "name": "SFR-Embedding-Mistral", "maker": "salesforce", "family": "salesforce/sfr", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval, clustering, classification, and semantic similarity. It was trained as a general text embedding model, not as a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1230, "dossier_id": "scut/tonggu/tonggu-vl-2b-instruct", "name": "TongGu-VL-2B-Instruct", "maker": "scut", "family": "scut/tonggu", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card describes a 2B multimodal model for classical-Chinese scenarios. The instruction mixture is CCS358k, with 7 scenarios.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1231, "dossier_id": "sensetime/character/sensechat-character-pro", "name": "SenseChat-Character-Pro", "maker": "sensetime", "family": "sensetime/character", "context_length": 32768, "prompt_price_per_m": 15.0, "completion_price_per_m": 15.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Longer in-character dialogue and story progression. Streaming requires n=1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1232, "dossier_id": "sensetime/sensechat/sensechat-32k", "name": "SenseChat-32K", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General chat when the 4K SenseChat window is too short and the later 128K SenseChat-5 is not required.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1233, "dossier_id": "sensetime/sensechat/sensechat-5-1202", "name": "SenseChat-5-1202", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1234, "dossier_id": "sensetime/sensechat/sensechat-5-cantonese", "name": "SenseChat-5-Cantonese", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 32768, "prompt_price_per_m": 27.0, "completion_price_per_m": 27.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cantonese conversation plus general knowledge, reasoning, math, and code.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1235, "dossier_id": "sensetime/sensechat/sensechat-5-lite", "name": "\u5546\u91cf\u901a\u7528\u8f7b\u91cf 5.0", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower-cost general chat, math, code, and commonsense tasks than the full SenseNova 5.0 model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1236, "dossier_id": "sensetime/sensechat/sensechat-turbo-1202", "name": "SenseChat-Turbo-1202", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower-cost general chat than SenseChat-5, with the December 2024 dialogue upgrades.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1237, "dossier_id": "servicenow/apriel/aprielguard", "name": "AprielGuard", "maker": "servicenow", "family": "servicenow/apriel", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1238, "dossier_id": "shanghai-ai-lab/internlm/internlm2-5-7b-chat", "name": "InternLM2.5-7B-Chat", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT and online RLHF chat model with instruction following, chat, and function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1239, "dossier_id": "shanghai-ai-lab/internlm/internlm3-8b-instruct", "name": "InternLM3-8B-Instruct", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1240, "dossier_id": "sjtu/abel/abel-7b-002", "name": "Abel-7B-002", "maker": "sjtu", "family": "sjtu/abel", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Math reasoning and general benchmarks. The card reports a 35% GSM8K and 126% MATH improvement over Abel-7B-001.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1241, "dossier_id": "sjtu/davinci/davinci-dev-32b", "name": "daVinci-Dev-32B", "maker": "sjtu", "family": "sjtu/davinci", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1242, "dossier_id": "sjtu/davinci/davinci-dev-72b", "name": "daVinci-Dev-72B", "maker": "sjtu", "family": "sjtu/davinci", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Agentic software engineering from a non-coder Qwen2.5 base, using agent-native mid-training and environment-native SFT.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1243, "dossier_id": "sjtu/davinci/davinci-kernel-14b", "name": "daVinci-kernel-14B", "maker": "sjtu", "family": "sjtu/davinci", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1244, "dossier_id": "sjtu/davinci/davinci-kernel-8b", "name": "daVinci-kernel-8B", "maker": "sjtu", "family": "sjtu/davinci", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1245, "dossier_id": "sjtu/davinci/davinci-origin-3b", "name": "daVinci-origin-3B", "maker": "sjtu", "family": "sjtu/davinci", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1246, "dossier_id": "sjtu/deepresearcher/deepresearcher-7b", "name": "DeepResearcher-7B", "maker": "sjtu", "family": "sjtu/deepresearcher", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1247, "dossier_id": "sjtu/limo/limo", "name": "LIMO", "maker": "sjtu", "family": "sjtu/limo", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1248, "dossier_id": "sjtu/limo/limo-v2", "name": "LIMO-v2", "maker": "sjtu", "family": "sjtu/limo", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1249, "dossier_id": "sjtu/reasoneval/reasoneval-7b", "name": "ReasonEval-7B", "maker": "sjtu", "family": "sjtu/reasoneval", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ReasonEval-7B is GAIR's 7B math-step judge. Status: active. Collection date 2026-10-15. Weights are Apache-2.0.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1250, "dossier_id": "sjtu/safety-j/safety-j", "name": "Safety-J", "maker": "sjtu", "family": "sjtu/safety-j", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1251, "dossier_id": "sk-telecom/a-x/a-x-3-1", "name": "A.X 3.1", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X 3.1 is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1252, "dossier_id": "snowflake/arctic-text2sql/arctic-text2sql-r1-7b", "name": "Arctic-Text2SQL-R1 7B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1253, "dossier_id": "stockmark/stockmark-2/stockmark-2-100b-instruct", "name": "Stockmark-2-100B-Instruct", "maker": "stockmark", "family": "stockmark/stockmark-2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1254, "dossier_id": "stockmark/stockmark-2/stockmark-2-vl-100b-beta", "name": "Stockmark-2-VL-100B-beta", "maker": "stockmark", "family": "stockmark/stockmark-2", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1255, "dossier_id": "tii/falcon-3/falcon3-10b", "name": "Falcon3-10B", "maker": "tii", "family": "tii/falcon-3", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1256, "dossier_id": "tii/falcon-3/falcon3-7b", "name": "Falcon3-7B", "maker": "tii", "family": "tii/falcon-3", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1257, "dossier_id": "tii/falcon-3/falcon3-mamba-7b", "name": "Falcon3-Mamba-7B", "maker": "tii", "family": "tii/falcon-3", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Updated pure state-space Falcon model with a longer context and more STEM and code data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1258, "dossier_id": "tii/falcon-3/falcon3-video", "name": "Falcon3 Video", "maker": "tii", "family": "tii/falcon-3", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video summarization and question answering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1259, "dossier_id": "tii/falcon-arabic/falcon-arabic-7b", "name": "Falcon-Arabic 7B", "maker": "tii", "family": "tii/falcon-arabic", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic-first chat and general language model, including dialects. Served on Falcon Chat, not as public weights.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1260, "dossier_id": "together/llama-2-32k/llama-2-7b-32k", "name": "LLaMA-2-7B-32K", "maker": "together", "family": "together/llama-2-32k", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released LLaMA-2-7B-32K (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1261, "dossier_id": "together/llama-2-32k/llama-2-7b-32k-instruct", "name": "Llama-2-7B-32K-Instruct", "maker": "together", "family": "together/llama-2-32k", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Llama-2-7B-32K-Instruct (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1262, "dossier_id": "together/m2-bert/m2-bert-80m-32k", "name": "M2-BERT-80M-32k", "maker": "together", "family": "together/m2-bert", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released M2-BERT-80M-32k (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1263, "dossier_id": "together/m2-bert/m2-bert-80m-32k-retrieval", "name": "M2-BERT-80M-32k-retrieval", "maker": "together", "family": "together/m2-bert", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released M2-BERT-80M-32k-retrieval (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1264, "dossier_id": "together/stripedhyena/stripedhyena-hessian-7b", "name": "StripedHyena-Hessian-7B", "maker": "together", "family": "together/stripedhyena", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released StripedHyena-Hessian-7B (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1265, "dossier_id": "together/stripedhyena/stripedhyena-nous-7b", "name": "StripedHyena-Nous-7B", "maker": "together", "family": "together/stripedhyena", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released StripedHyena-Nous-7B (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1266, "dossier_id": "together/tev1/tev1-4b-experimental", "name": "Tev1-4B-experimental", "maker": "together", "family": "together/tev1", "context_length": 32768, "prompt_price_per_m": 0.042, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Tev1-4B-experimental (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1267, "dossier_id": "tsinghua/minicpm/bitcpm4-0-5b", "name": "BitCPM4-0.5B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A ternary-quantized MiniCPM4 for the same end-side setting as the 0.5B dense model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1268, "dossier_id": "tsinghua/minicpm/minicpm3-4b", "name": "MiniCPM3-4B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1269, "dossier_id": "tsinghua/minicpm/minicpm4-0-5b", "name": "MiniCPM4-0.5B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The small end-side MiniCPM4, for on-device generation with the series' sparse-attention and system stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1270, "dossier_id": "tsinghua/minicpm/minicpm4-8b", "name": "MiniCPM4-8B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1271, "dossier_id": "tsinghua/minicpm-o/minicpm-o-2-6", "name": "MiniCPM-o 2.6", "maker": "tsinghua", "family": "tsinghua/minicpm-o", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1272, "dossier_id": "tsinghua/minicpm-v/minicpm-v-4", "name": "MiniCPM-V 4", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A mid-size MiniCPM-V generation between 2.6 and 4.5, with a SigLIP vision tower.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1273, "dossier_id": "ucsb/co-patcher/co-patcher-loc-gen-14b", "name": "Co-PatcheR-Loc-Gen-14B", "maker": "ucsb", "family": "ucsb/co-patcher", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1274, "dossier_id": "ucsb/co-patcher/co-patcher-val-assert-14b", "name": "Co-PatcheR-Val-assert-14B", "maker": "ucsb", "family": "ucsb/co-patcher", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1275, "dossier_id": "ucsb/co-patcher/co-patcher-val-no-assert-14b", "name": "Co-PatcheR-Val-no-assert-14B", "maker": "ucsb", "family": "ucsb/co-patcher", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1276, "dossier_id": "ucsb/termigen/termigen-32b", "name": "TermiGen-32B", "maker": "ucsb", "family": "ucsb/termigen", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1277, "dossier_id": "ucsb/vulnllm-r/vulnllm-r-7b", "name": "VulnLLM-R-7B", "maker": "ucsb", "family": "ucsb/vulnllm-r", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1278, "dossier_id": "uiuc/spatialreasoner/spatialreasoner-r1", "name": "SpatialReasoner-R1", "maker": "uiuc", "family": "uiuc/spatialreasoner", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1279, "dossier_id": "unbabel/tower/towerinstruct-mistral-7b-v0-2", "name": "TowerInstruct Mistral 7B v0.2", "maker": "unbabel", "family": "unbabel/tower", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1280, "dossier_id": "upstage/groundedness/groundedness-check", "name": "Groundedness Check", "maker": "upstage", "family": "upstage/groundedness", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Label whether an assistant answer is grounded, not grounded, or uncertain given a supplied context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1281, "dossier_id": "upstage/solar/solar-mini", "name": "Solar Mini", "maker": "upstage", "family": "upstage/solar", "context_length": 32768, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.15, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Upstage describes it as a lightweight instruction-following model for English, Korean, and Japanese production traffic.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1282, "dossier_id": "upstage/solar/solar-mini-ja", "name": "Solar Mini JA", "maker": "upstage", "family": "upstage/solar", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese chat, with function calling added in the January 2025 snapshot.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1283, "dossier_id": "upstage/solar/solar-pro", "name": "Solar Pro", "maker": "upstage", "family": "upstage/solar", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1284, "dossier_id": "upstage/syn/syn", "name": "Syn", "maker": "upstage", "family": "upstage/syn", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1285, "dossier_id": "upstage/tinysolar/tinysolar-248m-4k-code-instruct", "name": "TinySolar 248M 4K Code Instruct", "maker": "upstage", "family": "upstage/tinysolar", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1286, "dossier_id": "vivo/bluelm/bluelm-7b-base-32k", "name": "BlueLM-7B-Base-32K", "maker": "vivo", "family": "vivo/bluelm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1287, "dossier_id": "vivo/bluelm/bluelm-7b-chat-32k", "name": "BlueLM-7B-Chat-32K", "maker": "vivo", "family": "vivo/bluelm", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1288, "dossier_id": "vivo/bluelm-v/bluelm-2-5-3b", "name": "BlueLM-2.5-3B", "maker": "vivo", "family": "vivo/bluelm-v", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1289, "dossier_id": "writer/palmyra-med/palmyra-med-70b-32k", "name": "Palmyra-Med-70B-32K", "maker": "writer", "family": "writer/palmyra-med", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1290, "dossier_id": "xmu/ume-r1/ume-r1-2b", "name": "UME-R1-2B", "maker": "xmu", "family": "xmu/ume-r1", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1291, "dossier_id": "xmu/ume-r1/ume-r1-7b", "name": "UME-R1-7B", "maker": "xmu", "family": "xmu/ume-r1", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1292, "dossier_id": "yale/rtriever/rtriever-4b", "name": "RTriever-4B", "maker": "yale", "family": "yale/rtriever", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1293, "dossier_id": "yandex/yandexgpt/yandexgpt-4-pro", "name": "YandexGPT 4 Pro", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1294, "dossier_id": "yandex/yandexgpt/yandexgpt-5-1-pro", "name": "YandexGPT 5.1 Pro", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": 32768, "prompt_price_per_m": 0.41, "completion_price_per_m": 0.41, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1295, "dossier_id": "yandex/yandexgpt/yandexgpt-5-lite", "name": "YandexGPT 5 Lite", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": 32768, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.2, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1296, "dossier_id": "yandex/yandexgpt/yandexgpt-5-lite-pretrain", "name": "YandexGPT 5 Lite Pretrain", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": 32768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Further pretraining or instruction tuning. Yandex says it is not aligned for direct instruction following.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1297, "dossier_id": "yandex/yandexgpt/yandexgpt-5-pro", "name": "YandexGPT 5 Pro", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": 32768, "prompt_price_per_m": 1.22, "completion_price_per_m": 1.22, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1298, "dossier_id": "01-ai/yi-large/yi-large", "name": "Yi-Large", "maker": "01-ai", "family": "01-ai/yi-large", "context_length": 32000, "prompt_price_per_m": 3.0, "completion_price_per_m": 3.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1299, "dossier_id": "01-ai/yi-large/yi-large-fc", "name": "Yi-Large-FC", "maker": "01-ai", "family": "01-ai/yi-large", "context_length": 32000, "prompt_price_per_m": 3.0, "completion_price_per_m": 3.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1300, "dossier_id": "01-ai/yi-medium/yi-medium", "name": "Yi-Medium", "maker": "01-ai", "family": "01-ai/yi-medium", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The partner page says it is the balanced, lower-cost model for everyday chat, QA, collaboration, and translation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1301, "dossier_id": "alibaba/qwen/qwen-plus-character", "name": "Qwen-Plus-Character", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Role-play variant. The text index gives a 32k context and marks thinking, tools, and structured output unsupported.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1302, "dossier_id": "alibaba/qwen-omni/qwen-omni-turbo", "name": "Qwen-Omni-Turbo", "maker": "alibaba", "family": "alibaba/qwen-omni", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1303, "dossier_id": "deepseek/v2/v2-lite", "name": "DeepSeek-V2-Lite", "maker": "deepseek", "family": "deepseek/v2", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-V2-Lite is the small V2 model, released 16 May 2024, for local use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1304, "dossier_id": "google/gemini-robotics/gemini-robotics-1-5", "name": "Gemini Robotics 1.5", "maker": "google", "family": "google/gemini-robotics", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1305, "dossier_id": "microsoft/decision/microsoft-decision-1", "name": "Microsoft Decision 1", "maker": "microsoft", "family": "microsoft/decision", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Yes/no, multiple-choice, rating, classification, and rubric decisions in one call.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1306, "dossier_id": "microsoft/mai/mai-image-2-5", "name": "MAI-Image-2.5", "maker": "microsoft", "family": "microsoft/mai", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1307, "dossier_id": "microsoft/mai/mai-image-2-5-flash", "name": "MAI-Image-2.5-Flash", "maker": "microsoft", "family": "microsoft/mai", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1308, "dossier_id": "microsoft/mai/mai-image-2-5-pro", "name": "MAI-Image-2.5-Pro", "maker": "microsoft", "family": "microsoft/mai", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1309, "dossier_id": "microsoft/mai/mai-image-2-6", "name": "MAI-Image-2.6", "maker": "microsoft", "family": "microsoft/mai", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1310, "dossier_id": "microsoft/mai/mai-image-2-6-flash", "name": "MAI-Image-2.6-Flash", "maker": "microsoft", "family": "microsoft/mai", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation and image editing. Microsoft says edits can change part of an image without rebuilding the rest.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1311, "dossier_id": "naver/hyperclova-x/hcx-dash-002", "name": "HCX-DASH-002", "maker": "naver", "family": "naver/hyperclova-x", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1312, "dossier_id": "naver/hyperclova-x-seed/seed-omni-8b", "name": "HyperCLOVA X SEED Omni 8B", "maker": "naver", "family": "naver/hyperclova-x-seed", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1313, "dossier_id": "naver/hyperclova-x-seed/seed-think-14b", "name": "HyperCLOVA X SEED Think 14B", "maker": "naver", "family": "naver/hyperclova-x-seed", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1314, "dossier_id": "openai/realtime/gpt-4o-realtime-preview", "name": "GPT-4o Realtime", "maker": "openai", "family": "openai/realtime", "context_length": 32000, "prompt_price_per_m": 40.0, "completion_price_per_m": 80.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Model capable of realtime text and audio inputs and outputs", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1315, "dossier_id": "openai/realtime/gpt-realtime", "name": "GPT-Realtime", "maker": "openai", "family": "openai/realtime", "context_length": 32000, "prompt_price_per_m": 32.0, "completion_price_per_m": 64.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Model capable of realtime text and audio inputs and outputs", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1316, "dossier_id": "openai/realtime/gpt-realtime-1-5", "name": "GPT-Realtime-1.5", "maker": "openai", "family": "openai/realtime", "context_length": 32000, "prompt_price_per_m": 32.0, "completion_price_per_m": 64.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The best voice model for audio in, audio out", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1317, "dossier_id": "openai/realtime/gpt-realtime-mini", "name": "GPT-Realtime Mini", "maker": "openai", "family": "openai/realtime", "context_length": 32000, "prompt_price_per_m": 10.0, "completion_price_per_m": 20.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A cost-efficient version of GPT-Realtime", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1318, "dossier_id": "poolside/malibu/malibu-2-1", "name": "Malibu 2.1", "maker": "poolside", "family": "poolside/malibu", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1319, "dossier_id": "preferred-networks/plamo-2/plamo-2-0-prime", "name": "PLaMo 2.0 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "context_length": 32000, "prompt_price_per_m": 60.0, "completion_price_per_m": 250.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1320, "dossier_id": "sapienza/minerva/chatminerva", "name": "ChatMinerva", "maker": "sapienza", "family": "sapienza/minerva", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1321, "dossier_id": "stepfun/step/step-1-32k", "name": "Step-1 32K", "maker": "stepfun", "family": "stepfun/step", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same general language jobs as Step-1, with a longer shared context cap.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1322, "dossier_id": "stepfun/step/step-2-mini", "name": "Step-2 Mini", "maker": "stepfun", "family": "stepfun/step", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-cost general text and code, according to the text-model guide.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1323, "dossier_id": "stepfun/step-vision/step-1o-turbo-vision", "name": "Step-1o Turbo Vision", "maker": "stepfun", "family": "stepfun/step-vision", "context_length": 32000, "prompt_price_per_m": 2.5, "completion_price_per_m": 8.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "StepFun names social copy, assistants, and image or video understanding as the typical jobs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1324, "dossier_id": "stepfun/step-vision/step-1o-vision-32k", "name": "Step-1o Vision 32K", "maker": "stepfun", "family": "stepfun/step-vision", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1325, "dossier_id": "stepfun/step-vision/step-1v-32k", "name": "Step-1V 32K", "maker": "stepfun", "family": "stepfun/step-vision", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same vision-language role as Step-1V, with a larger shared context cap.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1326, "dossier_id": "voyage/rerank/rerank-2-5", "name": "rerank-2.5", "maker": "voyage", "family": "voyage/rerank", "context_length": 32000, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General reranking with instruction following. Legacy since rerank-3 became Active. Still served.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1327, "dossier_id": "voyage/rerank/rerank-2-5-lite", "name": "rerank-2.5-lite", "maker": "voyage", "family": "voyage/rerank", "context_length": 32000, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster instruction-following reranker. Replacement is rerank-3-lite.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1328, "dossier_id": "voyage/rerank/rerank-3", "name": "rerank-3", "maker": "voyage", "family": "voyage/rerank", "context_length": 32000, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Highest-accuracy general reranker. Voyage recommends it for most applications. Upgrade path from rerank-2.5 at the same price.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1329, "dossier_id": "voyage/rerank/rerank-3-lite", "name": "rerank-3-lite", "maker": "voyage", "family": "voyage/rerank", "context_length": 32000, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Latency-sensitive reranking. Recommended lite model. Upgrade path from rerank-2.5-lite.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1330, "dossier_id": "voyage/voyage/voyage-3", "name": "voyage-3", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": 0.06, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1331, "dossier_id": "voyage/voyage/voyage-3-5", "name": "voyage-3.5", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": 0.06, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Previous general-purpose and multilingual retrieval model. MongoDB replacement for new projects is voyage-4.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1332, "dossier_id": "voyage/voyage/voyage-3-5-lite", "name": "voyage-3.5-lite", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Previous latency- and cost-optimized general-purpose embedding. MongoDB replacement is voyage-4-lite.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1333, "dossier_id": "voyage/voyage/voyage-3-large", "name": "voyage-3-large", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": 0.18, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General-purpose and multilingual retrieval. Still callable. MongoDB recommends voyage-4-large for new work.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1334, "dossier_id": "voyage/voyage/voyage-3-lite", "name": "voyage-3-lite", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1335, "dossier_id": "voyage/voyage/voyage-4", "name": "voyage-4", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": 0.06, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1336, "dossier_id": "voyage/voyage/voyage-4-large", "name": "voyage-4-large", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1337, "dossier_id": "voyage/voyage/voyage-4-lite", "name": "voyage-4-lite", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1338, "dossier_id": "voyage/voyage/voyage-4-nano", "name": "voyage-4-nano", "maker": "voyage", "family": "voyage/voyage", "context_length": 32000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1339, "dossier_id": "voyage/voyage-code/voyage-code-3", "name": "voyage-code-3", "maker": "voyage", "family": "voyage/voyage-code", "context_length": 32000, "prompt_price_per_m": 0.18, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code retrieval. Still served. MongoDB replacement is voyage-code-4 as of 13 August 2026.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1340, "dossier_id": "voyage/voyage-code/voyage-code-4", "name": "voyage-code-4", "maker": "voyage", "family": "voyage/voyage-code", "context_length": 32000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1341, "dossier_id": "voyage/voyage-context/voyage-context-3", "name": "voyage-context-3", "maker": "voyage", "family": "voyage/voyage-context", "context_length": 32000, "prompt_price_per_m": 0.18, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chunk embeddings that include the rest of the document. Still served. Replacement is voyage-context-4.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1342, "dossier_id": "voyage/voyage-context/voyage-context-4", "name": "voyage-context-4", "maker": "voyage", "family": "voyage/voyage-context", "context_length": 32000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1343, "dossier_id": "voyage/voyage-finance/voyage-finance-2", "name": "voyage-finance-2", "maker": "voyage", "family": "voyage/voyage-finance", "context_length": 32000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1344, "dossier_id": "voyage/voyage-multilingual/voyage-multilingual-2", "name": "voyage-multilingual-2", "maker": "voyage", "family": "voyage/voyage-multilingual", "context_length": 32000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1345, "dossier_id": "voyage/voyage-multimodal/voyage-multimodal-3", "name": "voyage-multimodal-3", "maker": "voyage", "family": "voyage/voyage-multimodal", "context_length": 32000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-and-image retrieval, including PDF screenshots, slides, tables, and figures. Replacement is voyage-multimodal-3.5.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1346, "dossier_id": "voyage/voyage-multimodal/voyage-multimodal-3-5", "name": "voyage-multimodal-3.5", "maker": "voyage", "family": "voyage/voyage-multimodal", "context_length": 32000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1347, "dossier_id": "zhipu/glm/glm-z1-airx", "name": "GLM-Z1-AirX", "maker": "zhipu", "family": "zhipu/glm", "context_length": 32000, "prompt_price_per_m": 5.0, "completion_price_per_m": 5.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1348, "dossier_id": "zhipu/glm-v/glm-ocr", "name": "GLM-OCR", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 32000, "prompt_price_per_m": 0.03, "completion_price_per_m": 0.03, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1349, "dossier_id": "ruc/yulan-mini/yulan-mini", "name": "YuLan-Mini", "maker": "ruc", "family": "ruc/yulan-mini", "context_length": 28723, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1350, "dossier_id": "ruc/yulan-mini/yulan-mini-instruct", "name": "YuLan-Mini-Instruct", "maker": "ruc", "family": "ruc/yulan-mini", "context_length": 28723, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1351, "dossier_id": "novelai/novelai-lm/xialong", "name": "Xialong", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": 28672, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1352, "dossier_id": "weizmann/gluformer/gluformer", "name": "GluFormer", "maker": "weizmann", "family": "weizmann/gluformer", "context_length": 25000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1353, "dossier_id": "kentucky/kos/kos-v4-base", "name": "KOS-V4-Base", "maker": "kentucky", "family": "kentucky/kos", "context_length": 24576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1354, "dossier_id": "kentucky/kos/kos-v4-instruct", "name": "KOS-V4-Instruct", "maker": "kentucky", "family": "kentucky/kos", "context_length": 24576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1355, "dossier_id": "kentucky/kos/kos-v5-base", "name": "KOS-V5-Base", "maker": "kentucky", "family": "kentucky/kos", "context_length": 24576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1356, "dossier_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", "context_length": 24576, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1357, "dossier_id": "stepfun/step-prover/stepfun-prover-preview-32b", "name": "StepFun-Prover-Preview-32B", "maker": "stepfun", "family": "stepfun/step-prover", "context_length": 20480, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Formal theorem proving with a Lean REPL in the loop, rather than general chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1358, "dossier_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", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "16K chat variant of Yi-1.5-34B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1359, "dossier_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", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "16K chat variant of Yi-1.5-9B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1360, "dossier_id": "adept/fuyu/fuyu-8b", "name": "Fuyu-8B", "maker": "adept", "family": "adept/fuyu", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1361, "dossier_id": "adept/persimmon/persimmon-8b-base", "name": "Persimmon-8B-Base", "maker": "adept", "family": "adept/persimmon", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1362, "dossier_id": "adept/persimmon/persimmon-8b-chat", "name": "Persimmon-8B-Chat", "maker": "adept", "family": "adept/persimmon", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1363, "dossier_id": "alibaba/qwen-mt/qwen-mt-flash", "name": "Qwen-MT-Flash", "maker": "alibaba", "family": "alibaba/qwen-mt", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cheaper translation model at a 16k context.", "summary_intro": "Alibaba model Qwen-MT-Flash. Status: active. Collected 2026-10-15. Cheaper translation model at a 16k context.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1364, "dossier_id": "alibaba/qwen-mt/qwen-mt-lite", "name": "Qwen-MT-Lite", "maker": "alibaba", "family": "alibaba/qwen-mt", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest listed Qwen-MT model, 16k context, no tools.", "summary_intro": "Alibaba model Qwen-MT-Lite. Status: active. Collected 2026-10-15. Smallest listed Qwen-MT model, 16k context, no tools.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1365, "dossier_id": "alibaba/qwen-mt/qwen-mt-plus", "name": "Qwen-MT-Plus", "maker": "alibaba", "family": "alibaba/qwen-mt", "context_length": 16384, "prompt_price_per_m": 2.46, "completion_price_per_m": 7.37, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1366, "dossier_id": "allenai/led/led-base", "name": "LED base", "maker": "allenai", "family": "allenai/led", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-document sequence-to-sequence model.", "summary_intro": "LED base is Ai2's 16,384-token encoder-decoder, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1367, "dossier_id": "allenai/led/led-large", "name": "LED large", "maker": "allenai", "family": "allenai/led", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger long-document sequence-to-sequence model.", "summary_intro": "LED large is Ai2's larger 16,384-token encoder-decoder, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1368, "dossier_id": "allenai/led/led-large-arxiv", "name": "LED large arXiv", "maker": "allenai", "family": "allenai/led", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Summarize long scientific papers.", "summary_intro": "LED large arXiv is Ai2's paper-summarization finetune of LED large, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1369, "dossier_id": "bigscience/starcoder2/starcoder2-15b", "name": "StarCoder2-15B", "maker": "bigscience", "family": "bigscience/starcoder2", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1370, "dossier_id": "bigscience/starcoder2/starcoder2-15b-instruct-v0-1", "name": "StarCoder2-15B-Instruct v0.1", "maker": "bigscience", "family": "bigscience/starcoder2", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1371, "dossier_id": "bigscience/starcoder2/starcoder2-3b", "name": "StarCoder2-3B", "maker": "bigscience", "family": "bigscience/starcoder2", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1372, "dossier_id": "bigscience/starcoder2/starcoder2-7b", "name": "StarCoder2-7B", "maker": "bigscience", "family": "bigscience/starcoder2", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1373, "dossier_id": "cyberagent/calm/calm3-22b-chat", "name": "CyberAgentLM3-22B-Chat", "maker": "cyberagent", "family": "cyberagent/calm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1374, "dossier_id": "eleutherai/comma/comma-v0-1-1t", "name": "Comma v0.1-1T", "maker": "eleutherai", "family": "eleutherai/comma", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1375, "dossier_id": "eleutherai/comma/comma-v0-1-2t", "name": "Comma v0.1-2T", "maker": "eleutherai", "family": "eleutherai/comma", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A base model showing what two trillion tokens of the Common Pile produce, for research and further post-training.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1376, "dossier_id": "elyza/japanese-codellama/elyza-japanese-codellama-7b", "name": "ELYZA-japanese-CodeLlama-7b", "maker": "elyza", "family": "elyza/japanese-codellama", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1377, "dossier_id": "elyza/japanese-codellama/elyza-japanese-codellama-7b-instruct", "name": "ELYZA-japanese-CodeLlama-7b-instruct", "maker": "elyza", "family": "elyza/japanese-codellama", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1378, "dossier_id": "huawei/pangu-mm/pangu-mm-m2-img2txt-16k", "name": "Pangu-MM-M2-Img2Txt-16K", "maker": "huawei", "family": "huawei/pangu-mm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image understanding and image-text QA at 16K, with pretraining and fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1379, "dossier_id": "huggingface/smolvlm/smolvlm-base", "name": "SmolVLM-Base", "maker": "huggingface", "family": "huggingface/smolvlm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1380, "dossier_id": "huggingface/smolvlm/smolvlm-instruct", "name": "SmolVLM-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1381, "dossier_id": "huggingface/smolvlm/smolvlm-synthetic", "name": "SmolVLM-Synthetic", "maker": "huggingface", "family": "huggingface/smolvlm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Synthetic-data fine-tune of the 2B-class SmolVLM, between the base and the instruct checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1382, "dossier_id": "huggingface/starchat/starchat2-15b", "name": "StarChat2-15B", "maker": "huggingface", "family": "huggingface/starchat", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face H4's StarChat2 coding assistant on StarCoder2-15B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1383, "dossier_id": "ibm/granite-vision/granite-vision-3-1-2b-preview", "name": "granite-vision-3.1-2b-preview", "maker": "ibm", "family": "ibm/granite-vision", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1384, "dossier_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", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1385, "dossier_id": "lighton/lightonocr/lightonocr-2-1b", "name": "LightOnOCR-2-1B", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1386, "dossier_id": "lighton/lightonocr/lightonocr-2-1b-base", "name": "LightOnOCR-2-1B-base", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Supervised pre-RLVR checkpoint. The card says to start domain fine-tunes here.", "summary_intro": "Supervised pre-RLVR checkpoint. The card says to start domain fine-tunes here. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1387, "dossier_id": "lighton/lightonocr/lightonocr-2-1b-bbox", "name": "LightOnOCR-2-1B-bbox", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "RLVR checkpoint that predicts text plus bounding boxes for embedded images, with an IoU reward.", "summary_intro": "RLVR checkpoint that predicts text plus bounding boxes for embedded images, with an IoU reward. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1388, "dossier_id": "lighton/lightonocr/lightonocr-2-1b-bbox-base", "name": "LightOnOCR-2-1B-bbox-base", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pre-RLVR bbox checkpoint for fine-tuning localization.", "summary_intro": "Pre-RLVR bbox checkpoint for fine-tuning localization. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1389, "dossier_id": "lighton/lightonocr/lightonocr-2-1b-bbox-soup", "name": "LightOnOCR-2-1B-bbox-soup", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1390, "dossier_id": "lighton/lightonocr/lightonocr-2-1b-ocr-soup", "name": "LightOnOCR-2-1B-ocr-soup", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Task-arithmetic merge for OCR robustness. The paper describes merging the RL OCR weights back toward the base.", "summary_intro": "Task-arithmetic merge for OCR robustness. The paper describes merging the RL OCR weights back toward the base. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1391, "dossier_id": "lighton/lightonocr/lightonocr-3-1b", "name": "LightOnOCR-3-1B", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1392, "dossier_id": "liquid-ai/d1/d1-omni-600m", "name": "d1-omni-600M", "maker": "liquid-ai", "family": "liquid-ai/d1", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small open decision model for text, image, or speech. Released 7 October 2026. One pass, no generated tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1393, "dossier_id": "mbzuai/jais/jais-family-30b-16k", "name": "Jais Family 30B 16K", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1394, "dossier_id": "oslo/norbert/norbert4-base", "name": "NorBERT 4 base", "maker": "oslo", "family": "oslo/norbert", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1395, "dossier_id": "oslo/norbert/norbert4-large", "name": "NorBERT 4 large", "maker": "oslo", "family": "oslo/norbert", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1396, "dossier_id": "oslo/norbert/norbert4-small", "name": "NorBERT 4 small", "maker": "oslo", "family": "oslo/norbert", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1397, "dossier_id": "oslo/norbert/norbert4-xlarge", "name": "NorBERT 4 xlarge", "maker": "oslo", "family": "oslo/norbert", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1398, "dossier_id": "oslo/norbert/norbert4-xsmall", "name": "NorBERT 4 xsmall", "maker": "oslo", "family": "oslo/norbert", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1399, "dossier_id": "ruc/tablellm/tablellm-13b", "name": "TableLLM-13B", "maker": "ruc", "family": "ruc/tablellm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1400, "dossier_id": "ruc/tablellm/tablellm-7b", "name": "TableLLM-7B", "maker": "ruc", "family": "ruc/tablellm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1401, "dossier_id": "sapienza/minerva/minerva-1b-base-v1-0", "name": "Minerva 1B base v1.0", "maker": "sapienza", "family": "sapienza/minerva", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1402, "dossier_id": "sapienza/minerva/minerva-350m-base-v1-0", "name": "Minerva 350M base v1.0", "maker": "sapienza", "family": "sapienza/minerva", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1403, "dossier_id": "sapienza/minerva/minerva-3b-base-v1-0", "name": "Minerva 3B base v1.0", "maker": "sapienza", "family": "sapienza/minerva", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1404, "dossier_id": "servicenow/apriel/apriel-5b-base", "name": "Apriel-5B-Base", "maker": "servicenow", "family": "servicenow/apriel", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1405, "dossier_id": "servicenow/apriel/apriel-5b-instruct", "name": "Apriel-5B-Instruct", "maker": "servicenow", "family": "servicenow/apriel", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1406, "dossier_id": "servicenow/starcoder/starcoder2-15b", "name": "StarCoder2-15B", "maker": "servicenow", "family": "servicenow/starcoder", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1407, "dossier_id": "servicenow/starcoder/starcoder2-3b", "name": "StarCoder2-3B", "maker": "servicenow", "family": "servicenow/starcoder", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1408, "dossier_id": "servicenow/starcoder/starcoder2-7b", "name": "StarCoder2-7B", "maker": "servicenow", "family": "servicenow/starcoder", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1409, "dossier_id": "shanghai-ai-lab/internlm/internlm-20b", "name": "InternLM-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base model aimed at stronger understanding, reasoning, math, and code than InternLM-7B, with a 16k context via inference extrapolation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1410, "dossier_id": "shanghai-ai-lab/internlm/internlm-chat-20b", "name": "InternLM-Chat-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat and tool-use model with the same 16k extrapolated context as InternLM-20B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1411, "dossier_id": "sk-telecom/a-x/a-x-4-0-light", "name": "A.X 4.0 Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X 4.0 Light is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1412, "dossier_id": "sk-telecom/a-x/a-x-encoder-base", "name": "A.X Encoder", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X Encoder is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1413, "dossier_id": "softbank/sarashina2-2/sarashina2-2-ocr", "name": "Sarashina2.2-OCR", "maker": "softbank", "family": "softbank/sarashina2-2", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "End-to-end OCR for Japanese and English documents, including vertical text, tables, math, and figure boxes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1414, "dossier_id": "tau/sled/bart-base-sled", "name": "BART-base SLED", "maker": "tau", "family": "tau/sled", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1415, "dossier_id": "tau/sled/bart-large-sled", "name": "BART-large SLED", "maker": "tau", "family": "tau/sled", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1416, "dossier_id": "tau/sled/t5-v1-1-base-sled", "name": "T5-v1.1-base SLED", "maker": "tau", "family": "tau/sled", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1417, "dossier_id": "tii/falcon-h1/falcon-h1-0-5b", "name": "Falcon-H1 0.5B", "maker": "tii", "family": "tii/falcon-h1", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1418, "dossier_id": "zyphra/zamba2/zamba2-7b-instruct", "name": "Zamba2-7B-Instruct", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": 16384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The cards describe supervised fine-tuning plus preference optimization for instruction following and chat, using open chat datasets.", "summary_intro": "Zamba2-7B-Instruct is Zyphra's instruction-tuned Zamba2 model, Apache-2.0, released 2024-10-14. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1419, "dossier_id": "01-ai/yi-large/yi-large-rag", "name": "Yi-Large-RAG", "maker": "01-ai", "family": "01-ai/yi-large", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1420, "dossier_id": "01-ai/yi-lightning/yi-lightning", "name": "Yi-Lightning", "maker": "01-ai", "family": "01-ai/yi-lightning", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1421, "dossier_id": "01-ai/yi-vision/yi-vision", "name": "Yi-Vision", "maker": "01-ai", "family": "01-ai/yi-vision", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The maker says it is for image question answering, chart understanding, OCR, and visual reasoning over multiple images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1422, "dossier_id": "01-ai/yi-vision/yi-vision-v2", "name": "Yi-Vision-V2", "maker": "01-ai", "family": "01-ai/yi-vision", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1423, "dossier_id": "cohere/aya/c4ai-aya-vision-32b", "name": "Aya Vision 32B", "maker": "cohere", "family": "cohere/aya", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1424, "dossier_id": "cohere/aya/c4ai-aya-vision-8b", "name": "Aya Vision 8B", "maker": "cohere", "family": "cohere/aya", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1425, "dossier_id": "cohere/north/north-small-translate-1-0", "name": "North Small Translate", "maker": "cohere", "family": "cohere/north", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1426, "dossier_id": "deepseek/coder/coder-1-3b", "name": "DeepSeek-Coder-1.3B", "maker": "deepseek", "family": "deepseek/coder", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Coder 1.3B is the smallest model in the first coder release. Base and instruct weights are public.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1427, "dossier_id": "deepseek/coder/coder-33b", "name": "DeepSeek-Coder-33B", "maker": "deepseek", "family": "deepseek/coder", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Coder-33B is the largest first-generation coder. Coder-V2's paper uses it as the previous coder baseline.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1428, "dossier_id": "deepseek/coder/coder-6-7b", "name": "DeepSeek-Coder-6.7B", "maker": "deepseek", "family": "deepseek/coder", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Coder 6.7B is the mid-size first-generation coder. Base and instruct weights are public.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1429, "dossier_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", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1430, "dossier_id": "naver/hyperclova-x-seed/seed-vision-instruct-3b", "name": "HyperCLOVA X SEED Vision Instruct 3B", "maker": "naver", "family": "naver/hyperclova-x-seed", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1431, "dossier_id": "openai/audio/gpt-4o-mini-transcribe", "name": "GPT-4o Mini Transcribe", "maker": "openai", "family": "openai/audio", "context_length": 16000, "prompt_price_per_m": 1.25, "completion_price_per_m": 5.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speech-to-text model powered by GPT-4o Mini", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1432, "dossier_id": "openai/audio/gpt-4o-transcribe", "name": "GPT-4o Transcribe", "maker": "openai", "family": "openai/audio", "context_length": 16000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speech-to-text model powered by GPT-4o", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1433, "dossier_id": "openai/audio/gpt-4o-transcribe-diarize", "name": "GPT-4o Transcribe Diarize", "maker": "openai", "family": "openai/audio", "context_length": 16000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Transcription model that identifies who's speaking when", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1434, "dossier_id": "openai/realtime/gpt-4o-mini-realtime-preview", "name": "GPT-4o Mini Realtime", "maker": "openai", "family": "openai/realtime", "context_length": 16000, "prompt_price_per_m": 10.0, "completion_price_per_m": 20.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller realtime model for text and audio inputs and outputs", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1435, "dossier_id": "openai/realtime/gpt-realtime-translate", "name": "GPT-Realtime-Translate", "maker": "openai", "family": "openai/realtime", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming speech-to-speech translation model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1436, "dossier_id": "openai/realtime/gpt-realtime-whisper", "name": "GPT-Realtime-Whisper", "maker": "openai", "family": "openai/realtime", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming speech-to-text model for realtime transcription", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1437, "dossier_id": "preferred-networks/plamo/plamo-1-0-prime", "name": "PLaMo 1.0 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo", "context_length": 16000, "prompt_price_per_m": 300.0, "completion_price_per_m": 1000.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1438, "dossier_id": "preferred-networks/plamo-fin/plamo-fin-prime", "name": "PLaMo Fin Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-fin", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1439, "dossier_id": "stepfun/step/step-2-16k", "name": "Step-2 16K", "maker": "stepfun", "family": "stepfun/step", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1440, "dossier_id": "stepfun/step/step-2-16k-exp", "name": "Step-2 16K Exp", "maker": "stepfun", "family": "stepfun/step", "context_length": 16000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "StepFun described it as the place where new Step-2 features landed before the formal checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1441, "dossier_id": "voyage/rerank/rerank-2", "name": "rerank-2", "maker": "voyage", "family": "voyage/rerank", "context_length": 16000, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1442, "dossier_id": "voyage/voyage/voyage-large-2", "name": "voyage-large-2", "maker": "voyage", "family": "voyage/voyage", "context_length": 16000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1443, "dossier_id": "voyage/voyage/voyage-large-2-instruct", "name": "voyage-large-2-instruct", "maker": "voyage", "family": "voyage/voyage", "context_length": 16000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1444, "dossier_id": "voyage/voyage-code/voyage-code-2", "name": "voyage-code-2", "maker": "voyage", "family": "voyage/voyage-code", "context_length": 16000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1445, "dossier_id": "voyage/voyage-law/voyage-law-2", "name": "voyage-law-2", "maker": "voyage", "family": "voyage/voyage-law", "context_length": 16000, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1446, "dossier_id": "zhipu/glm-v/glm-4v-plus-0111", "name": "GLM-4V-Plus-0111", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 16000, "prompt_price_per_m": 4.0, "completion_price_per_m": 4.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1447, "dossier_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", "context_length": 11520, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1448, "dossier_id": "jina/reranker/jina-reranker-m0", "name": "jina-reranker-m0", "maker": "jina", "family": "jina/reranker", "context_length": 10240, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reranker for visual documents and for mixed text and image candidates, in 29+ languages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1449, "dossier_id": "northwestern/genomeocean/genomeocean-4b", "name": "GenomeOcean-4B", "maker": "northwestern", "family": "northwestern/genomeocean", "context_length": 10240, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1450, "dossier_id": "northwestern/genomeocean/genomeocean-4b-artificial-detector", "name": "GenomeOcean-4B Artificial Detector", "maker": "northwestern", "family": "northwestern/genomeocean", "context_length": 10240, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1451, "dossier_id": "northwestern/genomeocean/genomeocean-4b-bgcfm", "name": "GenomeOcean-4B-bgcFM", "maker": "northwestern", "family": "northwestern/genomeocean", "context_length": 10240, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1452, "dossier_id": "basque/lang-adapt/eu-qwen3-8b", "name": "Basque Qwen3 8B Base", "maker": "basque", "family": "basque/lang-adapt", "context_length": 8196, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1453, "dossier_id": "google/palm/text-bison", "name": "text-bison", "maker": "google", "family": "google/palm", "context_length": 8196, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation", "summary_intro": "Retired Google PaLM 2 API model text-bison. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1454, "dossier_id": "turku/register/web-register-multilingual-bge", "name": "Web register classifier, multilingual BGE", "maker": "turku", "family": "turku/register", "context_length": 8194, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1455, "dossier_id": "agh/bielik/bielik-1-5b-v3", "name": "Bielik-1.5B-v3", "maker": "agh", "family": "agh/bielik", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Polish base model for local and fine-tuning use. Chat use is Bielik-1.5B-v3.0-Instruct.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1456, "dossier_id": "agh/bielik/bielik-1-5b-v3-instruct", "name": "Bielik-1.5B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Polish instruct model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1457, "dossier_id": "agh/bielik/bielik-7b-v0-1", "name": "Bielik-7B-v0.1", "maker": "agh", "family": "agh/bielik", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1458, "dossier_id": "ai21/jurassic/jurassic-2-light", "name": "Jurassic-2 Light", "maker": "ai21", "family": "ai21/jurassic", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1459, "dossier_id": "ai21/jurassic/jurassic-2-mid", "name": "Jurassic-2 Mid", "maker": "ai21", "family": "ai21/jurassic", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1460, "dossier_id": "ai21/jurassic/jurassic-2-ultra", "name": "Jurassic-2 Ultra", "maker": "ai21", "family": "ai21/jurassic", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1461, "dossier_id": "aisingapore/sea-lion/gemma-sea-lion-v3-9b", "name": "Gemma-SEA-LION-v3-9B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base Gemma 2 9B SEA checkpoint before instruction tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1462, "dossier_id": "aisingapore/sea-lion/gemma-sea-lion-v3-9b-it", "name": "Gemma-SEA-LION-v3-9B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Best-performing SEA-LION v3 size on SEA-HELM among similar-sized models, according to the v3 overview.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1463, "dossier_id": "aisingapore/sea-lion/llama-sea-lion-v2-8b", "name": "Llama-SEA-LION-v2-8B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base SEA-LION v2 checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1464, "dossier_id": "aisingapore/sea-lion/llama-sea-lion-v2-8b-it", "name": "Llama-SEA-LION-v2-8B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction following for English, Indonesian, Tamil, Thai, and Vietnamese.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1465, "dossier_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", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Continued-pretrained 120B base (about 12B active) for Southeast Asian adaptation of Nemotron 3 Super.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1466, "dossier_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", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Continued-pretrained base for Southeast Asian QA, reasoning, code, and translation. It is not the chat checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1467, "dossier_id": "aleph-alpha/pharia/pharia-1-llm-7b-control", "name": "Pharia-1-LLM-7B-control", "maker": "aleph-alpha", "family": "aleph-alpha/pharia", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1468, "dossier_id": "aleph-alpha/pharia/pharia-1-llm-7b-control-aligned", "name": "Pharia-1-LLM-7B-control-aligned", "maker": "aleph-alpha", "family": "aleph-alpha/pharia", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1469, "dossier_id": "alibaba/qwen-embed/text-embedding-v3", "name": "Text-Embedding-v3", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Previous text embedding, kept for index compatibility.", "summary_intro": "Alibaba model Text-Embedding-v3. Status: active. Collected 2026-10-15. Previous text embedding, kept for index compatibility.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1470, "dossier_id": "alibaba/qwen-embed/text-embedding-v4", "name": "Text-Embedding-v4", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": 8192, "prompt_price_per_m": 0.07, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Default text embedding on Model Studio. The model info page says retrieval, clustering, and classification improved versus v3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1471, "dossier_id": "allenai/tulu/tulu-2-13b", "name": "Tulu 2 13B", "maker": "allenai", "family": "allenai/tulu", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mid-size Tulu 2 supervised chat model.", "summary_intro": "Tulu 2 13B is Ai2's November 2023 13B Llama 2 instruction model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1472, "dossier_id": "allenai/tulu/tulu-2-70b", "name": "Tulu 2 70B", "maker": "allenai", "family": "allenai/tulu", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest Tulu 2 supervised model.", "summary_intro": "Tulu 2 70B is Ai2's November 2023 70B Llama 2 instruction model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1473, "dossier_id": "allenai/tulu/tulu-2-7b", "name": "Tulu 2 7B", "maker": "allenai", "family": "allenai/tulu", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Supervised instruction model in the Tulu 2 suite.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1474, "dossier_id": "allenai/tulu/tulu-2-dpo-13b", "name": "Tulu 2 DPO 13B", "maker": "allenai", "family": "allenai/tulu", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preference-tuned 13B assistant.", "summary_intro": "Tulu 2 DPO 13B is Ai2's November 2023 13B preference-tuned chat model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1475, "dossier_id": "allenai/tulu/tulu-2-dpo-70b", "name": "Tulu 2 DPO 70B", "maker": "allenai", "family": "allenai/tulu", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flagship Tulu 2 assistant. The card calls it a strong alternative to Llama 2 70B Chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1476, "dossier_id": "allenai/tulu/tulu-2-dpo-7b", "name": "Tulu 2 DPO 7B", "maker": "allenai", "family": "allenai/tulu", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preference-tuned 7B assistant.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1477, "dossier_id": "amazon/nova/nova-multimodal-embeddings", "name": "Amazon Nova Multimodal Embeddings", "maker": "amazon", "family": "amazon/nova", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1478, "dossier_id": "amazon/titan/titan-embeddings-g1-text", "name": "Amazon Titan Embeddings G1 - Text", "maker": "amazon", "family": "amazon/titan", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1479, "dossier_id": "amazon/titan/titan-embeddings-g1-text-v2", "name": "Amazon Titan Embeddings G1 - Text v2", "maker": "amazon", "family": "amazon/titan", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1480, "dossier_id": "amazon/titan/titan-text-embeddings-v2", "name": "Amazon Titan Text Embeddings V2", "maker": "amazon", "family": "amazon/titan", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval, semantic search, clustering, classification, and RAG. Output dimension is configurable.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1481, "dossier_id": "amazon/titan/titan-text-express", "name": "Amazon Titan Text G1 - Express", "maker": "amazon", "family": "amazon/titan", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1482, "dossier_id": "apple/dclm/dclm-7b-8k", "name": "DCLM-7B-8k", "maker": "apple", "family": "apple/dclm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Longer-context open language model from the DCLM baseline line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1483, "dossier_id": "arcee/sec/llama-3-sec-base", "name": "Llama-3-SEC-Base", "maker": "arcee", "family": "arcee/sec", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Finance and regulatory analysis of SEC filings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1484, "dossier_id": "arcee/sec/llama-3-sec-chat", "name": "Llama-3-SEC-Chat", "maker": "arcee", "family": "arcee/sec", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Conversational analysis of SEC filings and related finance questions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1485, "dossier_id": "arcee/trinity/trinity-large-truebase", "name": "Trinity-Large-TrueBase", "maker": "arcee", "family": "arcee/trinity", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research, probing, ablations, and fine-tuning without pre-baked alignment.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1486, "dossier_id": "baai/aquila/aquila2-34b", "name": "Aquila2-34B", "maker": "baai", "family": "baai/aquila", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base model for long-context continuation after the June 2024 update.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1487, "dossier_id": "baai/bge/bge-m3", "name": "BGE-M3", "maker": "baai", "family": "baai/bge", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1488, "dossier_id": "baai/reranker/bge-reranker-v2-m3", "name": "bge-reranker-v2-m3", "maker": "baai", "family": "baai/reranker", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Rerank multilingual passages, including long inputs, after a bi-encoder such as bge-m3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1489, "dossier_id": "baidu/embedding/tao-8k", "name": "tao-8k", "maker": "baidu", "family": "baidu/embedding", "context_length": 8192, "prompt_price_per_m": 0.0005, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed a single long passage.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1490, "dossier_id": "berkeley/starling/starling-lm-7b-alpha", "name": "Starling-LM-7B-alpha", "maker": "berkeley", "family": "berkeley/starling", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1491, "dossier_id": "berkeley/starling/starling-lm-7b-beta", "name": "Starling-LM-7B-beta", "maker": "berkeley", "family": "berkeley/starling", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1492, "dossier_id": "bigscience/octopack/octocoder", "name": "OctoCoder", "maker": "bigscience", "family": "bigscience/octopack", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following code generation and edits. Prompts should be answerable as coding instructions. HumanEvalPack is the authors' eval.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1493, "dossier_id": "bigscience/starcoder/starcoder", "name": "StarCoder", "maker": "bigscience", "family": "bigscience/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1494, "dossier_id": "bigscience/starcoder/starcoderbase", "name": "StarCoderBase", "maker": "bigscience", "family": "bigscience/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1495, "dossier_id": "bigscience/starcoder/starcoderbase-1b", "name": "StarCoderBase-1B", "maker": "bigscience", "family": "bigscience/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1496, "dossier_id": "bigscience/starcoder/starcoderbase-3b", "name": "StarCoderBase-3B", "maker": "bigscience", "family": "bigscience/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1497, "dossier_id": "bigscience/starcoder/starcoderbase-7b", "name": "StarCoderBase-7B", "maker": "bigscience", "family": "bigscience/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1498, "dossier_id": "bigscience/starcoder/starcoderplus", "name": "StarCoderPlus", "maker": "bigscience", "family": "bigscience/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1499, "dossier_id": "bigscience/starcoder/tiny-starcoder-py", "name": "TinyStarCoderPy", "maker": "bigscience", "family": "bigscience/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Assisted code generation. The card says to prefer the 15B StarCoder or StarCoderBase models for pure completion.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1500, "dossier_id": "birmingham/l20-edu/l20-edu-135m", "name": "L20-Edu-135M", "maker": "birmingham", "family": "birmingham/l20-edu", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1501, "dossier_id": "cerebras/btlm/btlm-3b-8k-base", "name": "BTLM-3B-8K", "maker": "cerebras", "family": "cerebras/btlm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1502, "dossier_id": "cerebras/btlm/btlm-3b-8k-chat", "name": "BTLM-3B-8K Chat", "maker": "cerebras", "family": "cerebras/btlm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1503, "dossier_id": "cohere/parse/parse-v5-0", "name": "Parse 5", "maker": "cohere", "family": "cohere/parse", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1504, "dossier_id": "databricks/mpt/mpt-30b", "name": "MPT-30B", "maker": "databricks", "family": "databricks/mpt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1505, "dossier_id": "databricks/mpt/mpt-30b-chat", "name": "MPT-30B-Chat", "maker": "databricks", "family": "databricks/mpt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1506, "dossier_id": "databricks/mpt/mpt-30b-instruct", "name": "MPT-30B-Instruct", "maker": "databricks", "family": "databricks/mpt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-turn instruction following, including summarization and question answering. MosaicML built it to be commercially usable, unlike the chat tune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1507, "dossier_id": "databricks/mpt/mpt-7b-8k", "name": "MPT-7B-8k", "maker": "databricks", "family": "databricks/mpt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1508, "dossier_id": "databricks/mpt/mpt-7b-8k-chat", "name": "MPT-7B-8k-Chat", "maker": "databricks", "family": "databricks/mpt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1509, "dossier_id": "databricks/mpt/mpt-7b-8k-instruct", "name": "MPT-7B-8k-Instruct", "maker": "databricks", "family": "databricks/mpt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1510, "dossier_id": "dlut/tailing/tailing", "name": "TaiLing", "maker": "dlut", "family": "dlut/tailing", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1511, "dossier_id": "dlut/taiyi/taiyi", "name": "Taiyi", "maker": "dlut", "family": "dlut/taiyi", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1512, "dossier_id": "elyza/llama-3-jp/llama-3-elyza-jp-8b", "name": "Llama-3-ELYZA-JP-8B", "maker": "elyza", "family": "elyza/llama-3-jp", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1513, "dossier_id": "google/embeddinggemma/embeddinggemma-2", "name": "EmbeddingGemma 2", "maker": "google", "family": "google/embeddinggemma", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Map text, code, images, video, and audio into one vector space for retrieval and RAG.", "summary_intro": "Google DeepMind open multimodal embedding model released October 6, 2026. Collected 2026-10-15. Apache 2.0.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1514, "dossier_id": "google/gemini-embedding/gemini-embedding-2", "name": "Gemini Embedding 2", "maker": "google", "family": "google/gemini-embedding", "context_length": 8192, "prompt_price_per_m": 0.2, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google recommends it for cross-modal search, retrieval, and RAG.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1515, "dossier_id": "google/gemini-tts/gemini-2-5-flash-tts", "name": "Gemini 2.5 Flash TTS", "maker": "google", "family": "google/gemini-tts", "context_length": 8192, "prompt_price_per_m": 0.5, "completion_price_per_m": 10.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes it as fast, low-cost speech with control over style and pacing for assistants and narration.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1516, "dossier_id": "google/gemini-tts/gemini-2-5-pro-tts", "name": "Gemini 2.5 Pro TTS", "maker": "google", "family": "google/gemini-tts", "context_length": 8192, "prompt_price_per_m": 1.0, "completion_price_per_m": 20.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google aims it at podcasts, audiobooks, and other structured long-form speech.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1517, "dossier_id": "google/gemini-tts/gemini-3-1-flash-tts", "name": "Gemini 3.1 Flash TTS", "maker": "google", "family": "google/gemini-tts", "context_length": 8192, "prompt_price_per_m": 1.0, "completion_price_per_m": 20.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It generates steered speech with expressive tags. Google tells new workloads to use the Gemini 3.8 TTS models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1518, "dossier_id": "google/gemini-tts/gemini-3-8-flash-lite-tts", "name": "Gemini 3.8 Flash-Lite TTS", "maker": "google", "family": "google/gemini-tts", "context_length": 8192, "prompt_price_per_m": 0.5, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google positions it for high-volume production, voice-agent cascades, read-aloud, and voice replication.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1519, "dossier_id": "google/gemini-tts/gemini-3-8-flash-tts", "name": "Gemini 3.8 Flash TTS", "maker": "google", "family": "google/gemini-tts", "context_length": 8192, "prompt_price_per_m": 0.5, "completion_price_per_m": 9.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1520, "dossier_id": "google/gemma/gemma-2-2b", "name": "Gemma 2 2B", "maker": "google", "family": "google/gemma", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation, summarization, and extraction.", "summary_intro": "Google DeepMind open-weights Gemma 2 text model. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1521, "dossier_id": "google/gemma/gemma-2-9b", "name": "Gemma 2 9B", "maker": "google", "family": "google/gemma", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation, summarization, and extraction.", "summary_intro": "Google DeepMind open-weights Gemma 2 text model. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1522, "dossier_id": "hit/lychee/lychee-embed", "name": "Lychee-embed", "maker": "hit", "family": "hit/lychee", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1523, "dossier_id": "hit/lychee/lychee-rerank", "name": "Lychee-rerank", "maker": "hit", "family": "hit/lychee", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1524, "dossier_id": "hu-berlin/bueble/bueble-lm", "name": "B\u00fcbleLM", "maker": "hu-berlin", "family": "hu-berlin/bueble", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1525, "dossier_id": "huggingface/smollm/smollm2-1-7b", "name": "SmolLM2-1.7B", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 1.7B scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1526, "dossier_id": "huggingface/smollm/smollm2-1-7b-instruct", "name": "SmolLM2-1.7B Instruct", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 1.7B scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1527, "dossier_id": "huggingface/smollm/smollm2-135m", "name": "SmolLM2-135M", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 135M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1528, "dossier_id": "huggingface/smollm/smollm2-135m-instruct", "name": "SmolLM2-135M Instruct", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 135M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1529, "dossier_id": "huggingface/smollm/smollm2-360m", "name": "SmolLM2-360M", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 360M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1530, "dossier_id": "huggingface/smollm/smollm2-360m-instruct", "name": "SmolLM2-360M Instruct", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 360M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1531, "dossier_id": "huggingface/smolvlm/smolvlm-256m-base", "name": "SmolVLM-256M-Base", "maker": "huggingface", "family": "huggingface/smolvlm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1532, "dossier_id": "huggingface/smolvlm/smolvlm-256m-instruct", "name": "SmolVLM-256M-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned 256M SmolVLM for on-device image-text tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1533, "dossier_id": "huggingface/smolvlm/smolvlm-500m-base", "name": "SmolVLM-500M-Base", "maker": "huggingface", "family": "huggingface/smolvlm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1534, "dossier_id": "huggingface/smolvlm/smolvlm-500m-instruct", "name": "SmolVLM-500M-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned 500M SmolVLM.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1535, "dossier_id": "huggingface/smolvlm2/smolvlm2-2-2b-base", "name": "SmolVLM2-2.2B-Base", "maker": "huggingface", "family": "huggingface/smolvlm2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1536, "dossier_id": "huggingface/smolvlm2/smolvlm2-2-2b-instruct", "name": "SmolVLM2-2.2B-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1537, "dossier_id": "huggingface/smolvlm2/smolvlm2-256m-video-instruct", "name": "SmolVLM2-256M-Video-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest SmolVLM2 video instruct model. Same safetensors total as SmolVLM-256M-Instruct (256,484,928).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1538, "dossier_id": "huggingface/smolvlm2/smolvlm2-500m-video-instruct", "name": "SmolVLM2-500M-Video-Instruct", "maker": "huggingface", "family": "huggingface/smolvlm2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1539, "dossier_id": "huggingface/starchat/starchat-beta", "name": "StarChat-beta", "maker": "huggingface", "family": "huggingface/starchat", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The beta StarChat coding assistant from Hugging Face H4.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1540, "dossier_id": "huggingface/zephyr/zephyr-7b-gemma", "name": "Zephyr 7B Gemma", "maker": "huggingface", "family": "huggingface/zephyr", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zephyr recipe applied to Gemma 7B by Hugging Face H4.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1541, "dossier_id": "ibm/granite/granite-13b-chat-v2", "name": "Granite 13B Chat v2", "maker": "ibm", "family": "ibm/granite", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Granite 13B Chat v2 is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1542, "dossier_id": "ibm/granite/granite-13b-instruct-v2", "name": "Granite 13B Instruct v2", "maker": "ibm", "family": "ibm/granite", "context_length": 8192, "prompt_price_per_m": 0.6, "completion_price_per_m": 0.6, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Granite 13B Instruct v2 is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1543, "dossier_id": "ibm/granite/granite-20b-multilingual", "name": "Granite 20B Multilingual", "maker": "ibm", "family": "ibm/granite", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Granite 20B Multilingual is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "summary_intro": "Granite 20B Multilingual is an IBM granite model. Collection date 2026-10-15. Status: active. Recorded context window: 8192 tokens.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1544, "dossier_id": "ibm/granite/granite-swash-2b", "name": "Granite-SWASH-2B (Sliding Window Attention + Sinks Hybrid)", "maker": "ibm", "family": "ibm/granite", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1545, "dossier_id": "ibm/granite/granite-swash-3b-a600m", "name": "Granite-SWASH-3B-a600M (Sliding Window Attention + Sinks Hybrid)", "maker": "ibm", "family": "ibm/granite", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1546, "dossier_id": "ibm/granite-code/granite-20b-code-base-8k", "name": "Granite 20B Code Base 8K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1547, "dossier_id": "ibm/granite-code/granite-20b-code-base-r1-1", "name": "Granite-20B-Code-Base-r1.1", "maker": "ibm", "family": "ibm/granite-code", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1548, "dossier_id": "ibm/granite-code/granite-20b-code-instruct-8k", "name": "Granite 20B Code Instruct 8K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 8192, "prompt_price_per_m": 0.6, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1549, "dossier_id": "ibm/granite-code/granite-20b-code-instruct-r1-1", "name": "Granite-20B-Code-Instruct-r1.1", "maker": "ibm", "family": "ibm/granite-code", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1550, "dossier_id": "ibm/granite-code/granite-20b-functioncalling", "name": "Granite 20B Functioncalling", "maker": "ibm", "family": "ibm/granite-code", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1551, "dossier_id": "ibm/granite-code/granite-34b-code-base-8k", "name": "Granite 34B Code Base 8K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1552, "dossier_id": "ibm/granite-code/granite-34b-code-instruct-8k", "name": "Granite 34B Code Instruct 8K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 8192, "prompt_price_per_m": 0.6, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1553, "dossier_id": "ibm/granite-docling/granite-docling-258m", "name": "granite-docling-258m", "maker": "ibm", "family": "ibm/granite-docling", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1554, "dossier_id": "ibm/granite-embedding/granite-embedding-english-r2", "name": "Granite-Embedding-English-R2", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1555, "dossier_id": "ibm/granite-embedding/granite-embedding-reranker-english-r2", "name": "granite-embedding-reranker-english-r2", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1556, "dossier_id": "ibm/granite-embedding/granite-embedding-small-english-r2", "name": "Granite-Embedding-Small-English-R2", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1557, "dossier_id": "ibm/granite-guardian/granite-guardian-3-0-2b", "name": "Granite Guardian 3.0 2B", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1558, "dossier_id": "ibm/granite-guardian/granite-guardian-3-0-8b", "name": "Granite Guardian 3.0 8B", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1559, "dossier_id": "ibm/granite-timeseries/granite-timeseries-patchtst-fm-r1", "name": "Granite TimeSeries PatchTST-FM Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1560, "dossier_id": "ibm/granite-timeseries/granite-timeseries-patchtst-fm-r2", "name": "Granite TimeSeries PatchTST-FM-r2 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1561, "dossier_id": "iflytek/spark/spark-lite", "name": "Spark Lite", "maker": "iflytek", "family": "iflytek/spark", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1562, "dossier_id": "iflytek/spark/spark-max", "name": "Spark Max", "maker": "iflytek", "family": "iflytek/spark", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1563, "dossier_id": "iflytek/spark/spark-pro", "name": "Spark Pro", "maker": "iflytek", "family": "iflytek/spark", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1564, "dossier_id": "inria/modernbert-bio/modernbert-bio-base", "name": "ModernBERT-bio base", "maker": "inria", "family": "inria/modernbert-bio", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1565, "dossier_id": "inria/modernbert-bio/modernbert-bio-large", "name": "ModernBERT-bio large", "maker": "inria", "family": "inria/modernbert-bio", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1566, "dossier_id": "inria/moderncamembert/moderncamembert-base", "name": "ModernCamemBERT base", "maker": "inria", "family": "inria/moderncamembert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1567, "dossier_id": "inria/moderncamembert/moderncamembert-cv2-base", "name": "ModernCamemBERT-CV2 base", "maker": "inria", "family": "inria/moderncamembert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1568, "dossier_id": "inria/moderncamembert-bio/moderncamembert-bio-base", "name": "ModernCamemBERT-bio base", "maker": "inria", "family": "inria/moderncamembert-bio", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1569, "dossier_id": "inria/moderncamembert-bio/moderncamembert-bio-large", "name": "ModernCamemBERT-bio large", "maker": "inria", "family": "inria/moderncamembert-bio", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1570, "dossier_id": "inria/moderncamembert-bio/moderncamembert-bio-v2-base", "name": "ModernCamemBERT-bio v2 base", "maker": "inria", "family": "inria/moderncamembert-bio", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1571, "dossier_id": "inria/moderncamembert-bio/moderncamembert-bio-v2-large", "name": "ModernCamemBERT-bio v2 large", "maker": "inria", "family": "inria/moderncamembert-bio", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1572, "dossier_id": "jhu/ettin/ettin-decoder-150m", "name": "Ettin decoder 150M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1573, "dossier_id": "jhu/ettin/ettin-decoder-17m", "name": "Ettin decoder 17M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1574, "dossier_id": "jhu/ettin/ettin-decoder-1b", "name": "Ettin decoder 1B", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1575, "dossier_id": "jhu/ettin/ettin-decoder-32m", "name": "Ettin decoder 32M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1576, "dossier_id": "jhu/ettin/ettin-decoder-400m", "name": "Ettin decoder 400M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1577, "dossier_id": "jhu/ettin/ettin-decoder-68m", "name": "Ettin decoder 68M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1578, "dossier_id": "jhu/ettin/ettin-encoder-150m", "name": "Ettin encoder 150M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1579, "dossier_id": "jhu/ettin/ettin-encoder-17m", "name": "Ettin encoder 17M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1580, "dossier_id": "jhu/ettin/ettin-encoder-1b", "name": "Ettin encoder 1B", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1581, "dossier_id": "jhu/ettin/ettin-encoder-32m", "name": "Ettin encoder 32M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1582, "dossier_id": "jhu/ettin/ettin-encoder-400m", "name": "Ettin encoder 400M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1583, "dossier_id": "jhu/ettin/ettin-encoder-68m", "name": "Ettin encoder 68M", "maker": "jhu", "family": "jhu/ettin", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1584, "dossier_id": "jhu/mmbert/mmbert-base", "name": "mmBERT-base", "maker": "jhu", "family": "jhu/mmbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1585, "dossier_id": "jhu/mmbert/mmbert-small", "name": "mmBERT-small", "maker": "jhu", "family": "jhu/mmbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1586, "dossier_id": "jina/clip/jina-clip-v1", "name": "jina-clip-v1", "maker": "jina", "family": "jina/clip", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1587, "dossier_id": "jina/clip/jina-clip-v2", "name": "jina-clip-v2", "maker": "jina", "family": "jina/clip", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1588, "dossier_id": "jina/colbert/jina-colbert-v1-en", "name": "jina-colbert-v1-en", "maker": "jina", "family": "jina/colbert", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English ColBERT-style retriever that preceded the multilingual v2 model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1589, "dossier_id": "jina/colbert/jina-colbert-v2", "name": "jina-colbert-v2", "maker": "jina", "family": "jina/colbert", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual ColBERT late-interaction retriever for when token-level vectors matter more than a single dense vector.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1590, "dossier_id": "jina/embeddings/jina-embeddings-v2-base-code", "name": "jina-embeddings-v2-base-code", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code and natural-language-to-code embeddings from the v2 generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1591, "dossier_id": "jina/embeddings/jina-embeddings-v2-base-de", "name": "jina-embeddings-v2-base-de", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "German-English bilingual long-context embeddings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1592, "dossier_id": "jina/embeddings/jina-embeddings-v2-base-en", "name": "jina-embeddings-v2-base-en", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English text embeddings with an 8,192-token window, released as an open-weight peer to text-embedding-ada-002.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1593, "dossier_id": "jina/embeddings/jina-embeddings-v2-base-es", "name": "jina-embeddings-v2-base-es", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Spanish-English bilingual long-context embeddings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1594, "dossier_id": "jina/embeddings/jina-embeddings-v2-base-zh", "name": "jina-embeddings-v2-base-zh", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese-English bilingual long-context embeddings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1595, "dossier_id": "jina/embeddings/jina-embeddings-v2-large-en", "name": "jina-embeddings-v2-large-en", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Announced large English v2 embedding. No public weights were found.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1596, "dossier_id": "jina/embeddings/jina-embeddings-v2-small-en", "name": "jina-embeddings-v2-small-en", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest shipped Jina Embeddings v2 model: English, 8,192 tokens, built for low memory.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1597, "dossier_id": "jina/embeddings/jina-embeddings-v3", "name": "jina-embeddings-v3", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1598, "dossier_id": "jina/embeddings/jina-embeddings-v5-omni-nano", "name": "jina-embeddings-v5-omni-nano", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller omni embedding for text, image, audio, video, and PDF when the 1.7B-class model is too heavy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1599, "dossier_id": "jina/embeddings/jina-embeddings-v5-text-nano", "name": "jina-embeddings-v5-text-nano", "maker": "jina", "family": "jina/embeddings", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The smaller v5 text embedding, aimed at latency, cost, and edge deployment rather than the last point of accuracy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1600, "dossier_id": "jina/reranker/jina-reranker-v1-base-en", "name": "jina-reranker-v1-base-en", "maker": "jina", "family": "jina/reranker", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1601, "dossier_id": "jina/reranker/jina-reranker-v1-tiny-en", "name": "jina-reranker-v1-tiny-en", "maker": "jina", "family": "jina/reranker", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1602, "dossier_id": "jina/reranker/jina-reranker-v1-turbo-en", "name": "jina-reranker-v1-turbo-en", "maker": "jina", "family": "jina/reranker", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Legacy English reranker. Weights are Apache-2.0, so commercial self-hosting does not need the CC BY-NC waiver.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1603, "dossier_id": "kakao/kanana/kanana-8b", "name": "Kanana 8B", "maker": "kakao", "family": "kakao/kanana", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1604, "dossier_id": "kakao/kanana/kanana-nano-2-1b-base", "name": "Kanana Nano 2.1B Base", "maker": "kakao", "family": "kakao/kanana", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1605, "dossier_id": "kakao/kanana/kanana-nano-2-1b-instruct", "name": "Kanana Nano 2.1B Instruct", "maker": "kakao", "family": "kakao/kanana", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1606, "dossier_id": "kakao/kanana-safeguard/kanana-safeguard-8b", "name": "Kanana Safeguard 8B", "maker": "kakao", "family": "kakao/kanana-safeguard", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Korean card says it labels risk in user utterances or assistant replies inside a chat system.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1607, "dossier_id": "kakao/kanana-safeguard/kanana-safeguard-prompt-2-1b", "name": "Kanana Safeguard-Prompt 2.1B", "maker": "kakao", "family": "kakao/kanana-safeguard", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Kakao's Safeguard collection describes it as the model for prompt attacks, used with the 8B content and Siren models.", "summary_intro": "Kanana Safeguard-Prompt 2.1B is Kakao's open Korean prompt-attack detector. Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1608, "dossier_id": "korea-university/ko-gemma/ko-gemma-2b-v1", "name": "Ko-Gemma-2B-v1", "maker": "korea-university", "family": "korea-university/ko-gemma", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1609, "dossier_id": "korea-university/ko-gemma/ko-gemma-7b-v1", "name": "Ko-Gemma-7B-v1", "maker": "korea-university", "family": "korea-university/ko-gemma", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1610, "dossier_id": "korea-university/kure/kure-reranker-base", "name": "KURE-Reranker-base", "maker": "korea-university", "family": "korea-university/kure", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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\".", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1611, "dossier_id": "korea-university/kure/kure-reranker-nano", "name": "KURE-Reranker-nano", "maker": "korea-university", "family": "korea-university/kure", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1612, "dossier_id": "korea-university/kure/kure-sparse-base", "name": "KURE-Sparse-base", "maker": "korea-university", "family": "korea-university/kure", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1613, "dossier_id": "korea-university/kure/kure-sparse-nano", "name": "KURE-Sparse-nano", "maker": "korea-university", "family": "korea-university/kure", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1614, "dossier_id": "korea-university/kure/kure-v1", "name": "KURE-v1", "maker": "korea-university", "family": "korea-university/kure", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1615, "dossier_id": "korea-university/kure/kure-v2", "name": "KURE-v2", "maker": "korea-university", "family": "korea-university/kure", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1616, "dossier_id": "korea-university/lamar/lamar-600m", "name": "LAMAR-600M", "maker": "korea-university", "family": "korea-university/lamar", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1617, "dossier_id": "korea-university/mixlid/mixlid", "name": "MixLID", "maker": "korea-university", "family": "korea-university/mixlid", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1618, "dossier_id": "lightblue/qarasu/qarasu-14b-chat-plus-unleashed", "name": "Qarasu 14B Chat Plus Unleashed", "maker": "lightblue", "family": "lightblue/qarasu", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1619, "dossier_id": "lightblue/suzume/suzume-llama-3-8b-japanese", "name": "Suzume Llama 3 8B Japanese", "maker": "lightblue", "family": "lightblue/suzume", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1620, "dossier_id": "lightblue/suzume/suzume-llama-3-8b-multilingual", "name": "Suzume Llama 3 8B Multilingual", "maker": "lightblue", "family": "lightblue/suzume", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1621, "dossier_id": "lighton/alfred/alfred-40b-1023", "name": "Alfred-40B-1023", "maker": "lighton", "family": "lighton/alfred", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1622, "dossier_id": "lighton/colbert/agent-moderncolbert", "name": "Agent-ModernColBERT", "maker": "lighton", "family": "lighton/colbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1623, "dossier_id": "lighton/colbert/colbert-zero", "name": "ColBERT-Zero", "maker": "lighton", "family": "lighton/colbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1624, "dossier_id": "lighton/colbert/colbert-zero-supervised", "name": "ColBERT-Zero-supervised", "maker": "lighton", "family": "lighton/colbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named supervised stage of the ColBERT-Zero pipeline, released with the February 2026 paper.", "summary_intro": "Named supervised stage of the ColBERT-Zero pipeline, released with the February 2026 paper. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1625, "dossier_id": "lighton/colbert/colbert-zero-unsupervised", "name": "ColBERT-Zero-unsupervised", "maker": "lighton", "family": "lighton/colbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Unsupervised pretrain stage of ColBERT-Zero, released so others can study pretraining versus a dense-model initialization.", "summary_intro": "Unsupervised pretrain stage of ColBERT-Zero, released so others can study pretraining versus a dense-model initialization. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1626, "dossier_id": "lighton/colbert/gte-moderncolbert-v1", "name": "GTE-ModernColBERT-v1", "maker": "lighton", "family": "lighton/colbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1627, "dossier_id": "lighton/colbert/reason-moderncolbert", "name": "Reason-ModernColBERT", "maker": "lighton", "family": "lighton/colbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1628, "dossier_id": "lighton/denseon/denseon", "name": "DenseOn", "maker": "lighton", "family": "lighton/denseon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1629, "dossier_id": "lighton/denseon/denseon-unsupervised", "name": "DenseOn-unsupervised", "maker": "lighton", "family": "lighton/denseon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Stage-1 dense checkpoint of DenseOn, released for further fine-tuning.", "summary_intro": "Stage-1 dense checkpoint of DenseOn, released for further fine-tuning. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1630, "dossier_id": "lighton/lateon/lateon", "name": "LateOn", "maker": "lighton", "family": "lighton/lateon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1631, "dossier_id": "lighton/lateon/lateon-code", "name": "LateOn-Code", "maker": "lighton", "family": "lighton/lateon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1632, "dossier_id": "lighton/lateon/lateon-code-pretrain", "name": "LateOn-Code-pretrain", "maker": "lighton", "family": "lighton/lateon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrain checkpoint for LateOn-Code, trained on LightOn's cornstack-related data before supervised code retrieval fine-tuning.", "summary_intro": "Pretrain checkpoint for LateOn-Code, trained on LightOn's cornstack-related data before supervised code retrieval fine-tuning. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1633, "dossier_id": "lighton/lateon/lateon-hpool-regularized", "name": "LateOn-hpool-regularized", "maker": "lighton", "family": "lighton/lateon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1634, "dossier_id": "lighton/lateon/lateon-regularized", "name": "LateOn-regularized", "maker": "lighton", "family": "lighton/lateon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1635, "dossier_id": "lighton/lateon/lateon-unsupervised", "name": "LateOn-unsupervised", "maker": "lighton", "family": "lighton/lateon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Stage-1 contrastive checkpoint of LateOn, before supervised hard-negative fine-tuning. LightOn released it as a starting point.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1636, "dossier_id": "lighton/lightonocr/lightonocr-0-9b-16k-1025", "name": "LightOnOCR-0.9B-16k-1025", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1637, "dossier_id": "lighton/lightonocr/lightonocr-0-9b-32k-1025", "name": "LightOnOCR-0.9B-32k-1025", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "32k-vocabulary prune of LightOnOCR-1B-1025 for European languages. Context window on the text decoder remains 8192 tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1638, "dossier_id": "lighton/lightonocr/lightonocr-1b-1025", "name": "LightOnOCR-1B-1025", "maker": "lighton", "family": "lighton/lightonocr", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1639, "dossier_id": "lighton/mdenseon/mdenseon", "name": "mDenseOn", "maker": "lighton", "family": "lighton/mdenseon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "307M multilingual dense retriever released with mLateOn on 30 July 2026 for multilingual, long-document, English, and code retrieval.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1640, "dossier_id": "lighton/mdenseon/mdenseon-unsupervised", "name": "mDenseOn-unsupervised", "maker": "lighton", "family": "lighton/mdenseon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Unsupervised pretrain of mDenseOn.", "summary_intro": "Unsupervised pretrain of mDenseOn. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1641, "dossier_id": "lighton/mlateon/mlateon", "name": "mLateOn", "maker": "lighton", "family": "lighton/mlateon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1642, "dossier_id": "lighton/mlateon/mlateon-unsupervised", "name": "mLateOn-unsupervised", "maker": "lighton", "family": "lighton/mlateon", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Unsupervised pretrain of mLateOn, released as a fine-tuning starting point.", "summary_intro": "Unsupervised pretrain of mLateOn, released as a fine-tuning starting point. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1643, "dossier_id": "lighton/modernbert-embed/modernbert-embed-large", "name": "ModernBERT-embed-large", "maker": "lighton", "family": "lighton/modernbert-embed", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1644, "dossier_id": "lighton/modernbert-embed/modernbert-embed-large-unsupervised", "name": "ModernBERT-embed-large-unsupervised", "maker": "lighton", "family": "lighton/modernbert-embed", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contrastive pretrain checkpoint for ModernBERT-embed-large.", "summary_intro": "Contrastive pretrain checkpoint for ModernBERT-embed-large. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1645, "dossier_id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-230m", "name": "LFM2.5-Encoder-230M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller encoder in the 28 July 2026 pair, for the same fine-tune tasks as LFM2.5-Encoder-350M.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1646, "dossier_id": "liquid-ai/lfm2-5-nano/lfm2-5-encoder-350m", "name": "LFM2.5-Encoder-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1647, "dossier_id": "ljubljana/gams/gams-27b", "name": "GaMS-27B", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base text generation. The card groups it with GaMS-2B and GaMS-9B as the larger Gemma 2 GaMS models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1648, "dossier_id": "ljubljana/gams/gams-27b-instruct", "name": "GaMS-27B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following generation and translation. The card says this is the SFT version of GaMS-27B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1649, "dossier_id": "ljubljana/gams/gams-27b-instruct-nemotron", "name": "GaMS-27B-Instruct-Nemotron", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1650, "dossier_id": "ljubljana/gams/gams-2b", "name": "GaMS-2B", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1651, "dossier_id": "ljubljana/gams/gams-2b-instruct", "name": "GaMS-2B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following text generation. The card says this is the SFT version of the 2B GaMS model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1652, "dossier_id": "ljubljana/gams/gams-9b", "name": "GaMS-9B", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1653, "dossier_id": "ljubljana/gams/gams-9b-instruct", "name": "GaMS-9B-Instruct", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following generation and translation. The card evaluates English-Slovene translation in both directions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1654, "dossier_id": "ljubljana/gams/gams-9b-instruct-lex", "name": "GaMS-9B-Instruct-Lex", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene dictionary questions: meanings, synonyms, word forms, collocations, and contextual sense.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1655, "dossier_id": "ljubljana/gams/gams-9b-instruct-nemotron", "name": "GaMS-9B-Instruct-Nemotron", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction chat in Slovene and English after extra supervised fine-tuning on translated Nemotron chat data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1656, "dossier_id": "ljubljana/gams/gams-dpo-translator", "name": "GaMS-DPO-Translator", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-to-Slovene translation, with the card also showing a follow-up chat turn.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1657, "dossier_id": "llm-jp/llm-jp-modernbert/llm-jp-modernbert-base", "name": "LLM-jp ModernBERT base", "maker": "llm-jp", "family": "llm-jp/llm-jp-modernbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese fill-mask and encoder tasks. The card reports JGLUE, not chat benchmarks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1658, "dossier_id": "mbzuai/jais/jais-family-30b-8k", "name": "Jais Family 30B 8K", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1659, "dossier_id": "mbzuai/jais-2/jais-2-70b", "name": "Jais 2 70B", "maker": "mbzuai", "family": "mbzuai/jais-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1660, "dossier_id": "mbzuai/jais-2/jais-2-8b", "name": "Jais 2 8B", "maker": "mbzuai", "family": "mbzuai/jais-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1661, "dossier_id": "mbzuai/k2/k2-v2", "name": "K2-V2", "maker": "mbzuai", "family": "mbzuai/k2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1662, "dossier_id": "mbzuai/k2-type/k2-type-0-9b", "name": "K2-Type-0.9B", "maker": "mbzuai", "family": "mbzuai/k2-type", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1663, "dossier_id": "mbzuai/sherkala/sherkala-8b", "name": "Sherkala 8B", "maker": "mbzuai", "family": "mbzuai/sherkala", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is for Kazakh, with English, Russian, and Turkish retained, including chat assistants and Kazakh NLP research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1664, "dossier_id": "meta/llama/llama-3-70b", "name": "Llama 3 70B", "maker": "meta", "family": "meta/llama", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1665, "dossier_id": "meta/llama/llama-3-8b", "name": "Llama 3 8B", "maker": "meta", "family": "meta/llama", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1666, "dossier_id": "moonshot/moonlight/moonlight-16b-a3b", "name": "Moonlight-16B-A3B", "maker": "moonshot", "family": "moonshot/moonlight", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1667, "dossier_id": "moonshot/moonlight/moonlight-16b-a3b-instruct", "name": "Moonlight-16B-A3B-Instruct", "maker": "moonshot", "family": "moonshot/moonlight", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1668, "dossier_id": "moonshot/moonshot-v1/moonshot-v1-8k", "name": "Moonshot v1 8K", "maker": "moonshot", "family": "moonshot/moonshot-v1", "context_length": 8192, "prompt_price_per_m": 2.0, "completion_price_per_m": 10.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Short text. Official note: v1 models differ only by context length, not by quality.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1669, "dossier_id": "moonshot/moonshot-v1/moonshot-v1-8k-vision-preview", "name": "Moonshot v1 8K Vision", "maker": "moonshot", "family": "moonshot/moonshot-v1", "context_length": 8192, "prompt_price_per_m": 2.0, "completion_price_per_m": 10.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision preview. Understands images and returns text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1670, "dossier_id": "nagoya/ruri/ruri-v3-130m", "name": "Ruri v3 130M", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1671, "dossier_id": "nagoya/ruri/ruri-v3-30m", "name": "Ruri v3 30M", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1672, "dossier_id": "nagoya/ruri/ruri-v3-310m", "name": "Ruri v3 310M", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1673, "dossier_id": "nagoya/ruri/ruri-v3-70m", "name": "Ruri v3 70M", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1674, "dossier_id": "nagoya/ruri/ruri-v3-pt-130m", "name": "Ruri v3 PT 130M", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1675, "dossier_id": "nagoya/ruri/ruri-v3-pt-30m", "name": "Ruri v3 PT 30M", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1676, "dossier_id": "nagoya/ruri/ruri-v3-pt-310m", "name": "Ruri v3 PT 310M", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1677, "dossier_id": "nagoya/ruri/ruri-v3-pt-70m", "name": "Ruri v3 PT 70M", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1678, "dossier_id": "nagoya/ruri-reranker/ruri-v3-reranker-310m", "name": "Ruri v3 reranker 310M", "maker": "nagoya", "family": "nagoya/ruri-reranker", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1679, "dossier_id": "naver/hyperclova-x/hcx-003", "name": "HCX-003", "maker": "naver", "family": "naver/hyperclova-x", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1680, "dossier_id": "neu/lamate/lamate", "name": "LaMaTE", "maker": "neu", "family": "neu/lamate", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1681, "dossier_id": "niutrans/lamate/lamate", "name": "LaMaTE", "maker": "niutrans", "family": "niutrans/lamate", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1682, "dossier_id": "nomic/coderank/coderankembed", "name": "CodeRankEmbed", "maker": "nomic", "family": "nomic/coderank", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1683, "dossier_id": "nomic/embed-text/nomic-embed-text-v1", "name": "Nomic Embed Text v1", "maker": "nomic", "family": "nomic/embed-text", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English retrieval, similarity, clustering, and classification, including long documents up to 8192 tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1684, "dossier_id": "nomic/embed-text/nomic-embed-text-v1-5", "name": "Nomic Embed Text v1.5", "maker": "nomic", "family": "nomic/embed-text", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Production English embeddings when you need to shrink vectors (768 down to 64) or store them as bits.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1685, "dossier_id": "nous-research/minos/minos-v1", "name": "Minos v1", "maker": "nous-research", "family": "nous-research/minos", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1686, "dossier_id": "novelai/novelai-lm/clio", "name": "Clio", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1687, "dossier_id": "novelai/novelai-lm/kayra", "name": "Kayra", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1688, "dossier_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", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1689, "dossier_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", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1690, "dossier_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", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual content moderation for LLM applications. The card names English, Spanish, Mandarin, German, French, Hindi, Japanese, Arabic, and Thai.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1691, "dossier_id": "openai/gpt/computer-use-preview", "name": "computer-use-preview", "maker": "openai", "family": "openai/gpt", "context_length": 8192, "prompt_price_per_m": 3.0, "completion_price_per_m": 12.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized model for computer use tool", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1692, "dossier_id": "osu/tablellama/tablellama", "name": "TableLlama", "maker": "osu", "family": "osu/tablellama", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1693, "dossier_id": "preferred-networks/plamo/plamo-13b-instruct", "name": "PLaMo-13B-Instruct", "maker": "preferred-networks", "family": "preferred-networks/plamo", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese and English instruction following and dialogue, using only datasets the vendor could ship under a commercial license.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1694, "dossier_id": "preferred-networks/plamo/plamo-13b-instruct-nc", "name": "PLaMo-13B-Instruct-NC", "maker": "preferred-networks", "family": "preferred-networks/plamo", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research dialogue in Japanese and English when non-commercial instruction data is acceptable. Not licensed for commercial use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1695, "dossier_id": "prime-intellect/intellect/intellect-1", "name": "INTELLECT-1", "maker": "prime-intellect", "family": "prime-intellect/intellect", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1696, "dossier_id": "prime-intellect/intellect/intellect-1-instruct", "name": "INTELLECT-1-Instruct", "maker": "prime-intellect", "family": "prime-intellect/intellect", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1697, "dossier_id": "rinna/baku/gemma-2-baku-2b", "name": "Gemma 2 Baku 2B", "maker": "rinna", "family": "rinna/baku", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pretrain of Gemma 2 2B on about 80B tokens. Baku is the youkai \u734f.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1698, "dossier_id": "rinna/baku/gemma-2-baku-2b-it", "name": "Gemma 2 Baku 2B Instruct", "maker": "rinna", "family": "rinna/baku", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction model of Gemma 2 Baku 2B, made with a chat vector and ORPO.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1699, "dossier_id": "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-8k", "name": "Bilingual GPT-NeoX 4B 8K", "maker": "rinna", "family": "rinna/bilingual-gpt-neox", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1700, "dossier_id": "rinna/nekomata/nekomata-14b", "name": "Nekomata 14B", "maker": "rinna", "family": "rinna/nekomata", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pretrain of Qwen-14B on about 66B tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1701, "dossier_id": "rinna/nekomata/nekomata-14b-instruction", "name": "Nekomata 14B Instruction", "maker": "rinna", "family": "rinna/nekomata", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction tune of Nekomata 14B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1702, "dossier_id": "rinna/youko/llama-3-youko-70b", "name": "Llama 3 Youko 70B", "maker": "rinna", "family": "rinna/youko", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pretrain of Llama 3 70B on about 5B tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1703, "dossier_id": "rinna/youko/llama-3-youko-70b-instruct", "name": "Llama 3 Youko 70B Instruct", "maker": "rinna", "family": "rinna/youko", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1704, "dossier_id": "rinna/youko/llama-3-youko-8b", "name": "Llama 3 Youko 8B", "maker": "rinna", "family": "rinna/youko", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pretrain of Llama 3 8B on about 22B tokens. Youko is the youkai \u5996\u72d0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1705, "dossier_id": "rinna/youko/llama-3-youko-8b-instruct", "name": "Llama 3 Youko 8B Instruct", "maker": "rinna", "family": "rinna/youko", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction model of Llama 3 Youko 8B, built with SFT, a chat vector, and DPO.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1706, "dossier_id": "ruc/llama-3-syne/llama-3-syne", "name": "Llama-3-SynE", "maker": "ruc", "family": "ruc/llama-3-syne", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1707, "dossier_id": "ruc/yulan/yulan-chat-2-13b", "name": "YuLan-Chat-2-13B", "maker": "ruc", "family": "ruc/yulan", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1708, "dossier_id": "ruc/yulan/yulan-llama-2-13b", "name": "YuLan-LLaMA-2-13B", "maker": "ruc", "family": "ruc/yulan", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1709, "dossier_id": "salesforce/xgen/xgen-7b", "name": "XGen-7B", "maker": "salesforce", "family": "salesforce/xgen", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1710, "dossier_id": "sambanova/bloomchat/bloomchat-176b-v2", "name": "BLOOMChat 176B v2", "maker": "sambanova", "family": "sambanova/bloomchat", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual conversation, question answering, and long-sequence generative answers. The card still frames it as an early chat baseline.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1711, "dossier_id": "sambanova/sn/sn-13b-8k-instruct", "name": "SN-13B-8k-Instruct", "maker": "sambanova", "family": "sambanova/sn", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1712, "dossier_id": "sensetime/character/sensechat-character", "name": "SenseChat-Character", "maker": "sensetime", "family": "sensetime/character", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Casual in-character chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1713, "dossier_id": "sensetime/character/sensechat-character-turbo", "name": "SenseChat-Character-Turbo", "maker": "sensetime", "family": "sensetime/character", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "IP role play, group chat, knowledge-base replies, and function calls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1714, "dossier_id": "servicenow/starcoder/starcoder", "name": "StarCoder", "maker": "servicenow", "family": "servicenow/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1715, "dossier_id": "servicenow/starcoder/starcoderbase", "name": "StarCoderBase", "maker": "servicenow", "family": "servicenow/starcoder", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1716, "dossier_id": "shanghai-ai-lab/internlm/internlm-7b", "name": "InternLM-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base model trained on trillions of tokens for general Chinese and English use, with an 8k context window.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1717, "dossier_id": "shanghai-ai-lab/internlm/internlm-chat-7b", "name": "InternLM-Chat-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Practical chat model with an 8k window and a tool-building workflow described on the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1718, "dossier_id": "sjtu/autoj/autoj-13b", "name": "Auto-J-13B", "maker": "sjtu", "family": "sjtu/autoj", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1719, "dossier_id": "sjtu/confucius/confucius-confidence-verb", "name": "Confucius Confidence-Verb", "maker": "sjtu", "family": "sjtu/confucius", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1720, "dossier_id": "sjtu/confucius/confucius-multisample", "name": "Confucius Multisample", "maker": "sjtu", "family": "sjtu/confucius", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Honesty-aligned 13B checkpoint. The project README lists it beside confidence-verb as one of the two best released models.", "summary_intro": "Confucius Multisample is a GAIR 13B honesty-aligned model from the Alignment for Honesty project. Status: active. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1721, "dossier_id": "smu/2mamba/2mamba", "name": "2Mamba", "maker": "smu", "family": "smu/2mamba", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1722, "dossier_id": "snowflake/arctic-embed/arctic-embed-l-v2-0", "name": "Arctic Embed L v2.0", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1723, "dossier_id": "snowflake/arctic-embed/arctic-embed-l-v2-0-8k", "name": "Arctic Embed L v2.0 8k", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 8192, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1724, "dossier_id": "snowflake/arctic-embed/arctic-embed-m-long", "name": "Arctic Embed M Long", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1725, "dossier_id": "snowflake/arctic-embed/arctic-embed-m-v2-0", "name": "Arctic Embed M v2.0", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1726, "dossier_id": "softbank/diafill/diafill-sarashina2-2-3b-instruct", "name": "DiaFill Sarashina2.2-3B-Instruct", "maker": "softbank", "family": "softbank/diafill", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate chit-chat or call-center dialogue scripts from a seed with genre, industry, tone, and a summary.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1727, "dossier_id": "softbank/modernbert-ja/modernbert-ja-130m", "name": "ModernBERT-Ja-130M", "maker": "softbank", "family": "softbank/modernbert-ja", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked-language and encoder tasks such as classification, reranking features, and retrieval backbones. It is not a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1728, "dossier_id": "softbank/modernbert-ja/modernbert-ja-30m", "name": "ModernBERT-Ja-30M", "maker": "softbank", "family": "softbank/modernbert-ja", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked-language and encoder tasks such as classification, reranking features, and retrieval backbones. It is not a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1729, "dossier_id": "softbank/modernbert-ja/modernbert-ja-310m", "name": "ModernBERT-Ja-310M", "maker": "softbank", "family": "softbank/modernbert-ja", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked-language and encoder tasks such as classification, reranking features, and retrieval backbones. It is not a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1730, "dossier_id": "softbank/modernbert-ja/modernbert-ja-70m", "name": "ModernBERT-Ja-70M", "maker": "softbank", "family": "softbank/modernbert-ja", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked-language and encoder tasks such as classification, reranking features, and retrieval backbones. It is not a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1731, "dossier_id": "softbank/sarashina-embedding/sarashina-embedding-v1-1b", "name": "Sarashina-Embedding-v1-1B", "maker": "softbank", "family": "softbank/sarashina-embedding", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Semantic search, similarity, classification, clustering, and RAG retrieval for Japanese text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1732, "dossier_id": "softbank/sarashina-embedding/sarashina-embedding-v2-1b", "name": "Sarashina-Embedding-v2-1B", "maker": "softbank", "family": "softbank/sarashina-embedding", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Semantic search and RAG where a natural-language instruction can change the query vector.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1733, "dossier_id": "softbank/sarashina2/sarashina2-70b", "name": "Sarashina2-70B", "maker": "softbank", "family": "softbank/sarashina2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Released for research and industry analysis of Japanese LLMs. The card shows a completion example, not a chat template.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1734, "dossier_id": "softbank/sarashina2/sarashina2-8x70b", "name": "Sarashina2-8x70B", "maker": "softbank", "family": "softbank/sarashina2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1735, "dossier_id": "softbank/sarashina2-1/sarashina2-1-1b", "name": "Sarashina2.1-1B", "maker": "softbank", "family": "softbank/sarashina2-1", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1736, "dossier_id": "softbank/sarashina2-2/sarashina2-2-0-5b", "name": "Sarashina2.2-0.5B", "maker": "softbank", "family": "softbank/sarashina2-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrained base for research and for the published instruct, vision, OCR, speech, and embedding checkpoints that cite this line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1737, "dossier_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", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat and instruction following in Japanese. The base-model card points to this checkpoint as the post-trained release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1738, "dossier_id": "softbank/sarashina2-2/sarashina2-2-1b", "name": "Sarashina2.2-1B", "maker": "softbank", "family": "softbank/sarashina2-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrained base for research and for the published instruct, vision, OCR, speech, and embedding checkpoints that cite this line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1739, "dossier_id": "softbank/sarashina2-2/sarashina2-2-1b-instruct-v0-1", "name": "Sarashina2.2-1B-Instruct-v0.1", "maker": "softbank", "family": "softbank/sarashina2-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat and instruction following in Japanese. The base-model card points to this checkpoint as the post-trained release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1740, "dossier_id": "softbank/sarashina2-2/sarashina2-2-3b", "name": "Sarashina2.2-3B", "maker": "softbank", "family": "softbank/sarashina2-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrained base for research and for the published instruct, vision, OCR, speech, and embedding checkpoints that cite this line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1741, "dossier_id": "softbank/sarashina2-2/sarashina2-2-3b-instruct-v0-1", "name": "Sarashina2.2-3B-Instruct-v0.1", "maker": "softbank", "family": "softbank/sarashina2-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat and instruction following in Japanese. The base-model card points to this checkpoint as the post-trained release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1742, "dossier_id": "softbank/sarashina2-2/sarashina2-2-tts", "name": "Sarashina2.2-TTS", "maker": "softbank", "family": "softbank/sarashina2-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speech synthesis with pronunciation accuracy for Japanese, including kanji polyphony, as described in the linked paper.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1743, "dossier_id": "sony/sonido/sonido", "name": "SoniDo", "maker": "sony", "family": "sony/sonido", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1744, "dossier_id": "sustech/sus-chat/sus-chat-34b", "name": "SUS-Chat-34B", "maker": "sustech", "family": "sustech/sus-chat", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1745, "dossier_id": "tencent/hunyuan-mt/hunyuan-mt-7b", "name": "Hunyuan-MT-7B", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1746, "dossier_id": "tencent/hunyuan-mt/hunyuan-mt-chimera-7b", "name": "Hunyuan-MT-Chimera-7B", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1747, "dossier_id": "tencent/hunyuan-mt/hy-mt1-5-1-8b", "name": "HY-MT1.5-1.8B", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1748, "dossier_id": "tencent/hunyuan-mt/hy-mt1-5-7b", "name": "HY-MT1.5-7B", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1749, "dossier_id": "tencent/hunyuan-mt/hy-mt2-lite", "name": "Hy-MT2-Lite", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": 0.044, "completion_price_per_m": 0.177, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "TokenHub calls it the lightweight Hunyuan translation model for latency-sensitive calls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1750, "dossier_id": "tencent/hunyuan-mt/hy-mt2-plus", "name": "Hy-MT2-Plus", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": 0.074, "completion_price_per_m": 0.295, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "TokenHub calls it a translation model with strong instruction following.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1751, "dossier_id": "tencent/hunyuan-mt/hy-mt2-pro", "name": "Hy-MT2-Pro", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": 8192, "prompt_price_per_m": 0.074, "completion_price_per_m": 0.295, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "TokenHub calls it the flagship Hunyuan translation model for professional domains.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1752, "dossier_id": "tii/falcon-2/falcon-2-11b", "name": "Falcon 2 11B", "maker": "tii", "family": "tii/falcon-2", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1753, "dossier_id": "tii/falcon-3/falcon3-1b", "name": "Falcon3-1B", "maker": "tii", "family": "tii/falcon-3", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1754, "dossier_id": "tii/falcon-3/falcon3-3b", "name": "Falcon3-3B", "maker": "tii", "family": "tii/falcon-3", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1755, "dossier_id": "tii/falcon-mamba/falcon-mamba-7b", "name": "Falcon Mamba 7B", "maker": "tii", "family": "tii/falcon-mamba", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "TII's first open state-space language model, positioned as a general English base model and an instruct twin.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1756, "dossier_id": "together/evo/evo-1-8k-base", "name": "Evo-1 8k base", "maker": "together", "family": "together/evo", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Evo-1 8k base (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1757, "dossier_id": "together/m2-bert/m2-bert-80m-8k", "name": "M2-BERT-80M-8k", "maker": "together", "family": "together/m2-bert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released M2-BERT-80M-8k (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1758, "dossier_id": "together/m2-bert/m2-bert-80m-8k-retrieval", "name": "M2-BERT-80M-8k-retrieval", "maker": "together", "family": "together/m2-bert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released M2-BERT-80M-8k-retrieval (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1759, "dossier_id": "tohoku/bert-alpha/tohokunlp-bert-500m-sq8192-alpha", "name": "TohokuNLP BERT-alpha 500M (8192)", "maker": "tohoku", "family": "tohoku/bert-alpha", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1760, "dossier_id": "toronto/unbias-plus/qwen3-8b-unbias-plus-sft", "name": "Qwen3-8B-UnBias-Plus-SFT", "maker": "toronto", "family": "toronto/unbias-plus", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1761, "dossier_id": "toronto/unbias-plus/qwen3-8b-unbias-plus-sft-instruct", "name": "Qwen3-8B-UnBias-Plus-SFT-Instruct", "maker": "toronto", "family": "toronto/unbias-plus", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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:", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1762, "dossier_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", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1763, "dossier_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", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1764, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-de-en", "name": "Occiglot-7B-DE-EN", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1765, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-es-en", "name": "Occiglot-7B-ES-EN", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1766, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-eu5", "name": "Occiglot-7B-EU5", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1767, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-fr-en", "name": "Occiglot-7B-FR-EN", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1768, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-it-en", "name": "Occiglot-7B-IT-EN", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1769, "dossier_id": "turku/finnish-modernbert/finnish-modernbert-base-short", "name": "Finnish ModernBERT Base Short", "maker": "turku", "family": "turku/finnish-modernbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1770, "dossier_id": "turku/finnish-modernbert/finnish-modernbert-large-short", "name": "Finnish ModernBERT Large Short", "maker": "turku", "family": "turku/finnish-modernbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1771, "dossier_id": "turku/finnish-modernbert/finnish-modernbert-tiny-short", "name": "Finnish ModernBERT Tiny Short", "maker": "turku", "family": "turku/finnish-modernbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1772, "dossier_id": "u-tokyo/tanuki/tanuki-8b", "name": "Tanuki-8B", "maker": "u-tokyo", "family": "u-tokyo/tanuki", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1773, "dossier_id": "unbabel/tower-plus/tower-plus-2b", "name": "Tower+ 2B", "maker": "unbabel", "family": "unbabel/tower-plus", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1774, "dossier_id": "unbabel/tower-plus/tower-plus-9b", "name": "Tower+ 9B", "maker": "unbabel", "family": "unbabel/tower-plus", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1775, "dossier_id": "upstage/solar/solar-docvision", "name": "Solar DocVision Preview", "maker": "upstage", "family": "upstage/solar", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Document visual question answering. Upstage calls it experimental.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1776, "dossier_id": "uq/iter/iter-qwen3-embedding-0-6b", "name": "ITER Qwen3 Embedding 0.6B", "maker": "uq", "family": "uq/iter", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1777, "dossier_id": "uq/iter/iter-qwen3-embedding-4b", "name": "ITER Qwen3 Embedding 4B", "maker": "uq", "family": "uq/iter", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1778, "dossier_id": "writer/palmyra-med/palmyra-med-70b", "name": "Palmyra-Med-70B", "maker": "writer", "family": "writer/palmyra-med", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1779, "dossier_id": "wuerzburg/llammlein2vec/llammlein2vec-120m", "name": "LL\u00e4Mmlein2Vec 120M", "maker": "wuerzburg", "family": "wuerzburg/llammlein2vec", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1780, "dossier_id": "wuerzburg/llammlein2vec/llammlein2vec-1b", "name": "LL\u00e4Mmlein2Vec 1B", "maker": "wuerzburg", "family": "wuerzburg/llammlein2vec", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1781, "dossier_id": "wuerzburg/llammlein2vec/llammlein2vec-7b", "name": "LL\u00e4Mmlein2Vec 7B", "maker": "wuerzburg", "family": "wuerzburg/llammlein2vec", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1782, "dossier_id": "wuerzburg/moderngbert/moderngbert-134m", "name": "ModernGBERT 134M", "maker": "wuerzburg", "family": "wuerzburg/moderngbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1783, "dossier_id": "wuerzburg/moderngbert/moderngbert-1b", "name": "ModernGBERT 1B", "maker": "wuerzburg", "family": "wuerzburg/moderngbert", "context_length": 8192, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1784, "dossier_id": "alibaba/qwen/qwen-flash-character", "name": "Qwen-Flash-Character", "maker": "alibaba", "family": "alibaba/qwen", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small role-play model at an 8k context.", "summary_intro": "Alibaba model Qwen-Flash-Character. Status: active. Collected 2026-10-15. Small role-play model at an 8k context.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1785, "dossier_id": "alibaba/qwen-embed/qwen3-vl-rerank", "name": "Qwen3-VL-Rerank", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1786, "dossier_id": "cohere/aya/c4ai-aya-expanse-8b", "name": "Aya Expanse 8B", "maker": "cohere", "family": "cohere/aya", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1787, "dossier_id": "cohere/aya/tiny-aya-earth", "name": "Tiny Aya Earth", "maker": "cohere", "family": "cohere/aya", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1788, "dossier_id": "cohere/aya/tiny-aya-fire", "name": "Tiny Aya Fire", "maker": "cohere", "family": "cohere/aya", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1789, "dossier_id": "cohere/aya/tiny-aya-global", "name": "Tiny Aya Global", "maker": "cohere", "family": "cohere/aya", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1790, "dossier_id": "cohere/aya/tiny-aya-water", "name": "Tiny Aya Water", "maker": "cohere", "family": "cohere/aya", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1791, "dossier_id": "cohere/command/command-a-translate-08-2025", "name": "Command A Translate", "maker": "cohere", "family": "cohere/command", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1792, "dossier_id": "inflection/pi/inflection-3-pi", "name": "Inflection 3 Pi", "maker": "inflection", "family": "inflection/pi", "context_length": 8000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1793, "dossier_id": "inflection/productivity/inflection-3-productivity", "name": "Inflection 3 Productivity", "maker": "inflection", "family": "inflection/productivity", "context_length": 8000, "prompt_price_per_m": 2.5, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1794, "dossier_id": "softbank/sarashina3/sarashina3-embedding", "name": "Sarashina3 embedding", "maker": "softbank", "family": "softbank/sarashina3", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "RAG candidate retrieval and other Japanese semantic tasks, with a required task type that changes how the text is embedded.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1795, "dossier_id": "softbank/sarashina3/sarashina3-rerank", "name": "Sarashina3 rerank", "maker": "softbank", "family": "softbank/sarashina3", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1796, "dossier_id": "stepfun/step/step-1-8k", "name": "Step-1 8K", "maker": "stepfun", "family": "stepfun/step", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General writing, cross-language chat, question answering, and logic, according to the legacy text guide.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1797, "dossier_id": "stepfun/step-vision/step-1v-8k", "name": "Step-1V 8K", "maker": "stepfun", "family": "stepfun/step-vision", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1798, "dossier_id": "upstage/embedding/solar-embedding-2-passage", "name": "Solar Embedding 2 Passage", "maker": "upstage", "family": "upstage/embedding", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed documents to be searched. 8,000 token context, 1,024 dimensions. No alias yet.", "summary_intro": "Solar Embedding 2 Passage is Upstage's closed passage embedding model, collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1799, "dossier_id": "upstage/embedding/solar-embedding-2-query", "name": "Solar Embedding 2 Query", "maker": "upstage", "family": "upstage/embedding", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed user questions for retrieval. 8,000 token context, 1,024 dimensions. No alias yet; call the full id.", "summary_intro": "Solar Embedding 2 Query is Upstage's closed query embedding model, collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1800, "dossier_id": "voyage/rerank/rerank-1", "name": "rerank-1", "maker": "voyage", "family": "voyage/rerank", "context_length": 8000, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Quality-oriented first-generation reranker. Still listed as an older API model. Not on the Atlas lifecycle table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1801, "dossier_id": "voyage/rerank/rerank-2-lite", "name": "rerank-2-lite", "maker": "voyage", "family": "voyage/rerank", "context_length": 8000, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1802, "dossier_id": "xai/grok/grok-1", "name": "Grok-1", "maker": "xai", "family": "xai/grok", "context_length": 8000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1803, "dossier_id": "zhipu/embedding/embedding-2", "name": "Embedding-2", "maker": "zhipu", "family": "zhipu/embedding", "context_length": 8000, "prompt_price_per_m": 0.5, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Embedding-2 is a embedding model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1804, "dossier_id": "zhipu/embedding/embedding-3", "name": "Embedding-3", "maker": "zhipu", "family": "zhipu/embedding", "context_length": 8000, "prompt_price_per_m": 0.5, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Embedding-3 is a embedding model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1805, "dossier_id": "zhipu/glm/glm-4-airx", "name": "GLM-4-AirX", "maker": "zhipu", "family": "zhipu/glm", "context_length": 8000, "prompt_price_per_m": 10.0, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1806, "dossier_id": "innsbruck/reason-mxbai/reason-mxbai-colbert-32m", "name": "Reason-mxbai-ColBERT-32M", "maker": "innsbruck", "family": "innsbruck/reason-mxbai", "context_length": 7999, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1807, "dossier_id": "lighton/lateon/lateon-code-edge", "name": "LateOn-Code-edge", "maker": "lighton", "family": "lighton/lateon", "context_length": 7999, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1808, "dossier_id": "lighton/lateon/lateon-code-edge-pretrain", "name": "LateOn-Code-edge-pretrain", "maker": "lighton", "family": "lighton/lateon", "context_length": 7999, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrain checkpoint for LateOn-Code-edge, continuing a Mixedbread edge ColBERT on LightOn's code pretrain mixture.", "summary_intro": "Pretrain checkpoint for LateOn-Code-edge, continuing a Mixedbread edge ColBERT on LightOn's code pretrain mixture. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1809, "dossier_id": "hku/openphone/openphone", "name": "OpenPhone", "maker": "hku", "family": "hku/openphone", "context_length": 6144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1810, "dossier_id": "pisa/class-it/class-it", "name": "CLASS-IT", "maker": "pisa", "family": "pisa/class-it", "context_length": 6144, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1811, "dossier_id": "bit/earth-onevision/earth-onevision", "name": "Earth-OneVision", "maker": "bit", "family": "bit/earth-onevision", "context_length": 5400, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1812, "dossier_id": "tohoku/sumi/sumi-7b", "name": "Sumi-7B", "maker": "tohoku", "family": "tohoku/sumi", "context_length": 4864, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1813, "dossier_id": "bar-ilan/cdlm/cdlm", "name": "CDLM", "maker": "bar-ilan", "family": "bar-ilan/cdlm", "context_length": 4098, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrained cross-document language modeling for English multi-document tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1814, "dossier_id": "bar-ilan/lingmess/lingmess", "name": "LingMess", "maker": "bar-ilan", "family": "bar-ilan/lingmess", "context_length": 4098, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Coreference resolution with separate scorers for linguistically grouped mention pairs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1815, "dossier_id": "01-ai/yi/yi-34b", "name": "Yi-34B", "maker": "01-ai", "family": "01-ai/yi", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1816, "dossier_id": "01-ai/yi/yi-34b-chat", "name": "Yi-34B-Chat", "maker": "01-ai", "family": "01-ai/yi", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1817, "dossier_id": "01-ai/yi/yi-6b", "name": "Yi-6B", "maker": "01-ai", "family": "01-ai/yi", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The maker says the 6B series is suitable for personal and academic use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1818, "dossier_id": "01-ai/yi/yi-6b-chat", "name": "Yi-6B-Chat", "maker": "01-ai", "family": "01-ai/yi", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat companion to Yi-6B for personal and academic use, including the quantized weights meant for consumer GPUs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1819, "dossier_id": "01-ai/yi/yi-9b", "name": "Yi-9B", "maker": "01-ai", "family": "01-ai/yi", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1820, "dossier_id": "01-ai/yi-1-5/yi-1-5-34b", "name": "Yi-1.5-34B", "maker": "01-ai", "family": "01-ai/yi-1-5", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1821, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1822, "dossier_id": "01-ai/yi-1-5/yi-1-5-6b", "name": "Yi-1.5-6B", "maker": "01-ai", "family": "01-ai/yi-1-5", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "6B base. The official model list has no 16K or 32K 6B checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1823, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest Yi-1.5 chat model. No 16K chat sibling was in the official list.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1824, "dossier_id": "01-ai/yi-1-5/yi-1-5-9b", "name": "Yi-1.5-9B", "maker": "01-ai", "family": "01-ai/yi-1-5", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1825, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "9B chat model. The card calls it the top performer among similarly sized open chat models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1826, "dossier_id": "agh/bielik/bielik-7b-instruct-v0-1", "name": "Bielik-7B-Instruct-v0.1", "maker": "agh", "family": "agh/bielik", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1827, "dossier_id": "allenai/bolmo/bolmo-1b", "name": "Bolmo 1B", "maker": "allenai", "family": "allenai/bolmo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Byte-level fully open language model at about 1B parameters.", "summary_intro": "Bolmo 1B is Ai2's December 2025 byte-level language model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1828, "dossier_id": "allenai/emo/emo-1b14b-1t", "name": "EMO 1B-14B 1T", "maker": "allenai", "family": "allenai/emo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open MoE trained so reusable modules emerge without predefined domain labels. Paper id on the card is arXiv 2605.06663.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1829, "dossier_id": "allenai/flexolmo/flexolmo-7x7b-1t", "name": "FlexOlmo 7x7B 1T", "maker": "allenai", "family": "allenai/flexolmo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "FlexOlmo 7x7B 1T is Ai2's July 2025 mixture-of-experts model for data collaboration, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1830, "dossier_id": "allenai/longformer/longformer-base", "name": "Longformer base", "maker": "allenai", "family": "allenai/longformer", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Encode documents up to 4,096 tokens.", "summary_intro": "Longformer base is Ai2's 4,096-token long-document encoder, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1831, "dossier_id": "allenai/longformer/longformer-large", "name": "Longformer large", "maker": "allenai", "family": "allenai/longformer", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger long-document encoder.", "summary_intro": "Longformer large is Ai2's larger 4,096-token Longformer, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1832, "dossier_id": "allenai/molmo/molmo-7b-d", "name": "Molmo 7B-D", "maker": "allenai", "family": "allenai/molmo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Strong 7B Molmo demo model for image understanding and pointing. The public demo used Molmo-7B-D.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1833, "dossier_id": "allenai/olmo/olmo-7b-0724", "name": "OLMo 7B 0724", "maker": "allenai", "family": "allenai/olmo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "July 2024 OLMo 7B base, the Dolma 1.7-era checkpoint used as a fully open 7B baseline.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1834, "dossier_id": "allenai/olmo-2/olmo-2-1b", "name": "OLMo 2 1B", "maker": "allenai", "family": "allenai/olmo-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest OLMo 2 base model, for local research and recipe scaling checks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1835, "dossier_id": "allenai/olmo-2/olmo-2-1b-instruct", "name": "OLMo 2 1B Instruct", "maker": "allenai", "family": "allenai/olmo-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned OLMo 2 1B using the Tulu 3-style SFT, DPO, and RLVR stages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1836, "dossier_id": "allenai/olmo-2/olmo-2-32b", "name": "OLMo 2 32B", "maker": "allenai", "family": "allenai/olmo-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1837, "dossier_id": "allenai/olmo-2/olmo-2-32b-instruct", "name": "OLMo 2 32B Instruct", "maker": "allenai", "family": "allenai/olmo-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1838, "dossier_id": "allenai/olmo-2/olmo-2-7b", "name": "OLMo 2 7B", "maker": "allenai", "family": "allenai/olmo-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1839, "dossier_id": "allenai/olmo-2/olmo-2-7b-instruct", "name": "OLMo 2 7B Instruct", "maker": "allenai", "family": "allenai/olmo-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned OLMo 2 7B. Ai2 applied the Tulu 3 post-training recipe.", "summary_intro": "OLMo 2 7B Instruct is Ai2's post-trained 7B OLMo 2 chat model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1840, "dossier_id": "allenai/olmoe/olmoe-1b-7b-0125", "name": "OLMoE 1B-7B 0125", "maker": "allenai", "family": "allenai/olmoe", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fully open mixture-of-experts model. Active parameters stay near 1B while total parameters are about 6.9B.", "summary_intro": "OLMoE 1B-7B 0125 is an Ai2 OLMoE checkpoint, Apache-2.0, 64 experts with 8 active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1841, "dossier_id": "allenai/olmoe/olmoe-1b-7b-0125-instruct", "name": "OLMoE 1B-7B 0125 Instruct", "maker": "allenai", "family": "allenai/olmoe", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fully open mixture-of-experts model. Active parameters stay near 1B while total parameters are about 6.9B.", "summary_intro": "OLMoE 1B-7B 0125 Instruct is an Ai2 OLMoE checkpoint, Apache-2.0, 64 experts with 8 active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1842, "dossier_id": "allenai/olmoe/olmoe-1b-7b-0924", "name": "OLMoE 1B-7B 0924", "maker": "allenai", "family": "allenai/olmoe", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fully open mixture-of-experts model. Active parameters stay near 1B while total parameters are about 6.9B.", "summary_intro": "OLMoE 1B-7B 0924 is an Ai2 OLMoE checkpoint, Apache-2.0, 64 experts with 8 active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1843, "dossier_id": "allenai/olmoe/olmoe-1b-7b-0924-instruct", "name": "OLMoE 1B-7B 0924 Instruct", "maker": "allenai", "family": "allenai/olmoe", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fully open mixture-of-experts model. Active parameters stay near 1B while total parameters are about 6.9B.", "summary_intro": "OLMoE 1B-7B 0924 Instruct is an Ai2 OLMoE checkpoint, Apache-2.0, 64 experts with 8 active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1844, "dossier_id": "apple/afm/afm-on-device-2025", "name": "AFM on-device (2025)", "maker": "apple", "family": "apple/afm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1845, "dossier_id": "baichuan/baichuan/baichuan-13b-base", "name": "Baichuan-13B-Base", "maker": "baichuan", "family": "baichuan/baichuan", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1846, "dossier_id": "baichuan/baichuan/baichuan-13b-chat", "name": "Baichuan-13B-Chat", "maker": "baichuan", "family": "baichuan/baichuan", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1847, "dossier_id": "baichuan/baichuan/baichuan-7b", "name": "Baichuan-7B", "maker": "baichuan", "family": "baichuan/baichuan", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1848, "dossier_id": "baichuan/baichuan2/baichuan2-13b-base", "name": "Baichuan2-13B-Base", "maker": "baichuan", "family": "baichuan/baichuan2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1849, "dossier_id": "baichuan/baichuan2/baichuan2-13b-chat", "name": "Baichuan2-13B-Chat", "maker": "baichuan", "family": "baichuan/baichuan2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1850, "dossier_id": "baichuan/baichuan2/baichuan2-7b-base", "name": "Baichuan2-7B-Base", "maker": "baichuan", "family": "baichuan/baichuan2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1851, "dossier_id": "baichuan/baichuan2/baichuan2-7b-chat", "name": "Baichuan2-7B-Chat", "maker": "baichuan", "family": "baichuan/baichuan2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1852, "dossier_id": "bar-ilan/neodictabert/neodictabert", "name": "NeoDictaBERT", "maker": "bar-ilan", "family": "bar-ilan/neodictabert", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "NeoDictaBERT is a Hebrew NeoBERT-style encoder from Dicta, with Bar-Ilan coauthors. Public weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1853, "dossier_id": "bar-ilan/neodictabert/neodictabert-bilingual", "name": "NeoDictaBERT-bilingual", "maker": "bar-ilan", "family": "bar-ilan/neodictabert", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "NeoDictaBERT-bilingual is a Hebrew NeoBERT-style encoder from Dicta, with Bar-Ilan coauthors. Public weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1854, "dossier_id": "basque/latxa/latxa-13b", "name": "Latxa 13B", "maker": "basque", "family": "basque/latxa", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1855, "dossier_id": "basque/latxa/latxa-70b", "name": "Latxa 70B", "maker": "basque", "family": "basque/latxa", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1856, "dossier_id": "basque/latxa/latxa-7b", "name": "Latxa 7B", "maker": "basque", "family": "basque/latxa", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1857, "dossier_id": "berkeley/gorilla/gorilla-openfunctions-v1", "name": "Gorilla OpenFunctions v1", "maker": "berkeley", "family": "berkeley/gorilla", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1858, "dossier_id": "berkeley/gorilla/gorilla-openfunctions-v2", "name": "Gorilla OpenFunctions v2", "maker": "berkeley", "family": "berkeley/gorilla", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1859, "dossier_id": "bonn/lilmoo/lilmoo-v0-1", "name": "LilMoo-v0.1", "maker": "bonn", "family": "bonn/lilmoo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1860, "dossier_id": "bonn/lilmoo/lilmoo-v0-2", "name": "LilMoo-v0.2", "maker": "bonn", "family": "bonn/lilmoo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1861, "dossier_id": "bonn/liltii/liltii-v0-1", "name": "LilTii-v0.1", "maker": "bonn", "family": "bonn/liltii", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1862, "dossier_id": "bonn/liltii/liltii-v0-2", "name": "LilTii-v0.2", "maker": "bonn", "family": "bonn/liltii", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1863, "dossier_id": "bonn/tucano/tucano-2b4", "name": "Tucano-2b4", "maker": "bonn", "family": "bonn/tucano", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1864, "dossier_id": "bonn/tucano/tucano-2b4-instruct", "name": "Tucano-2b4-Instruct", "maker": "bonn", "family": "bonn/tucano", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1865, "dossier_id": "bonn/tucano2/tucano2-0-6b-base", "name": "Tucano2-0.6B-Base", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1866, "dossier_id": "bonn/tucano2/tucano2-qwen-0-5b-base", "name": "Tucano2-qwen-0.5B-Base", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1867, "dossier_id": "bonn/tucano2/tucano2-qwen-0-5b-instruct", "name": "Tucano2-qwen-0.5B-Instruct", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1868, "dossier_id": "bonn/tucano2/tucano2-qwen-0-5b-think", "name": "Tucano2-qwen-0.5B-Think", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1869, "dossier_id": "bonn/tucano2/tucano2-qwen-1-5b-base", "name": "Tucano2-qwen-1.5B-Base", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1870, "dossier_id": "bonn/tucano2/tucano2-qwen-1-5b-instruct", "name": "Tucano2-qwen-1.5B-Instruct", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1871, "dossier_id": "bonn/tucano2/tucano2-qwen-1-5b-think", "name": "Tucano2-qwen-1.5B-Think", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1872, "dossier_id": "bonn/tucano2/tucano2-qwen-3-7b-base", "name": "Tucano2-qwen-3.7B-Base", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1873, "dossier_id": "bonn/tucano2/tucano2-qwen-3-7b-instruct", "name": "Tucano2-qwen-3.7B-Instruct", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1874, "dossier_id": "bonn/tucano2/tucano2-qwen-3-7b-think", "name": "Tucano2-qwen-3.7B-Think", "maker": "bonn", "family": "bonn/tucano2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1875, "dossier_id": "bonn/vitucano/vitucano-2b8-v1", "name": "ViTucano-2b8-v1", "maker": "bonn", "family": "bonn/vitucano", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1876, "dossier_id": "cnrs/jargon/jargon-biomed-4096", "name": "Jargon biomed 4096", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1877, "dossier_id": "cnrs/jargon/jargon-legal-4096", "name": "Jargon legal 4096", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1878, "dossier_id": "cnrs/jargon/jargon-nachos-4096", "name": "Jargon NACHOS 4096", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1879, "dossier_id": "cohere/rerank/rerank-english-v3-0", "name": "Rerank English v3", "maker": "cohere", "family": "cohere/rerank", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The English-only Rerank 3.0 model. Cohere's index still lists it for English documents and JSON.", "summary_intro": "The English-only Rerank 3.0 model. Cohere's index still lists it for English documents and JSON. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1880, "dossier_id": "cohere/rerank/rerank-multilingual-v3-0", "name": "Rerank Multilingual v3", "maker": "cohere", "family": "cohere/rerank", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The multilingual Rerank 3.0 model for non-English documents and JSON. It is still listed on Cohere's index.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1881, "dossier_id": "cohere/rerank/rerank-v3-5", "name": "Rerank 3.5", "maker": "cohere", "family": "cohere/rerank", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1882, "dossier_id": "cornell/co-lmlm/co-lmlm-360m-fw", "name": "Co-LMLM-360M-FW", "maker": "cornell", "family": "cornell/co-lmlm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1883, "dossier_id": "cyberagent/calm/calm2-7b", "name": "CyberAgentLM2-7B", "maker": "cyberagent", "family": "cyberagent/calm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1884, "dossier_id": "cyberagent/reward/ca-reward-3b-ja", "name": "CA Reward 3B Japanese", "maker": "cyberagent", "family": "cyberagent/reward", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1885, "dossier_id": "deepseek/llm/llm-67b", "name": "DeepSeek-LLM-67B", "maker": "deepseek", "family": "deepseek/llm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-LLM 67B is the large first general model. DeepSeek compared later V2 against it.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1886, "dossier_id": "deepseek/llm/llm-7b", "name": "DeepSeek-LLM-7B", "maker": "deepseek", "family": "deepseek/llm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1887, "dossier_id": "deepseek/math/math-7b", "name": "DeepSeekMath-7B", "maker": "deepseek", "family": "deepseek/math", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1888, "dossier_id": "deepseek/vl/vl2", "name": "DeepSeek-VL2", "maker": "deepseek", "family": "deepseek/vl", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-VL2 is the largest VL2 model.", "summary_intro": "DeepSeek released DeepSeek-VL2. As of 2026-10-15 this catalog marks it active. DeepSeek-VL2 is the largest VL2 model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1889, "dossier_id": "deepseek/vl/vl2-small", "name": "DeepSeek-VL2-Small", "maker": "deepseek", "family": "deepseek/vl", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-VL2-Small is the middle VL2 model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1890, "dossier_id": "deepseek/vl/vl2-tiny", "name": "DeepSeek-VL2-Tiny", "maker": "deepseek", "family": "deepseek/vl", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-VL2-Tiny is the smallest VL2 model, for visual question answering, OCR, documents, charts, and grounding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1891, "dossier_id": "ecnu/educhat/educhat-base-002-13b-baichuan", "name": "EduChat-Base-002-13B-Baichuan", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1892, "dossier_id": "ecnu/educhat/educhat-sft-002-13b-baichuan", "name": "EduChat-SFT-002-13B-Baichuan", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1893, "dossier_id": "edinburgh/eurollm/eurollm-1-7b", "name": "EuroLLM-1.7B", "maker": "edinburgh", "family": "edinburgh/eurollm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1894, "dossier_id": "edinburgh/eurollm/eurollm-1-7b-instruct", "name": "EuroLLM-1.7B-Instruct", "maker": "edinburgh", "family": "edinburgh/eurollm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1895, "dossier_id": "edinburgh/eurollm/eurollm-9b", "name": "EuroLLM-9B", "maker": "edinburgh", "family": "edinburgh/eurollm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1896, "dossier_id": "edinburgh/eurollm/eurollm-9b-instruct", "name": "EuroLLM-9B-Instruct", "maker": "edinburgh", "family": "edinburgh/eurollm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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)\".", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1897, "dossier_id": "eleutherai/llemma/llemma-34b", "name": "Llemma 34B", "maker": "eleutherai", "family": "eleutherai/llemma", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same math-research role as Llemma 7B. The blog highlights chain-of-thought MATH solutions and in-context formal proving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1898, "dossier_id": "eleutherai/llemma/llemma-7b", "name": "Llemma 7B", "maker": "eleutherai", "family": "eleutherai/llemma", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1899, "dossier_id": "elyza/japanese-llama-2/elyza-japanese-llama-2-13b", "name": "ELYZA-japanese-Llama-2-13b", "maker": "elyza", "family": "elyza/japanese-llama-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1900, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1901, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1902, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1903, "dossier_id": "elyza/japanese-llama-2/elyza-japanese-llama-2-7b", "name": "ELYZA-japanese-Llama-2-7b", "maker": "elyza", "family": "elyza/japanese-llama-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1904, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster Japanese generation. ELYZA added Japanese vocabulary during continued pre-training so each sentence uses fewer tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1905, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1906, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1907, "dossier_id": "epfl/meditron/meditron-70b", "name": "Meditron 70B", "maker": "epfl", "family": "epfl/meditron", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1908, "dossier_id": "eth/apertus/apertus-1-1-0-5b", "name": "Apertus 1.1 0.5B", "maker": "eth", "family": "eth/apertus", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.1 0.5B (apertus-1-1-0-5b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1909, "dossier_id": "eth/apertus/apertus-1-1-0-5b-instruct", "name": "Apertus 1.1 0.5B Instruct", "maker": "eth", "family": "eth/apertus", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.1 0.5B Instruct (apertus-1-1-0-5b-instruct). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1910, "dossier_id": "eth/apertus/apertus-1-1-1-5b", "name": "Apertus 1.1 1.5B", "maker": "eth", "family": "eth/apertus", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.1 1.5B (apertus-1-1-1-5b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1911, "dossier_id": "eth/apertus/apertus-1-1-1-5b-instruct", "name": "Apertus 1.1 1.5B Instruct", "maker": "eth", "family": "eth/apertus", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.1 1.5B Instruct (apertus-1-1-1-5b-instruct). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1912, "dossier_id": "eth/apertus/apertus-1-1-4b", "name": "Apertus 1.1 4B", "maker": "eth", "family": "eth/apertus", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.1 4B (apertus-1-1-4b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1913, "dossier_id": "eth/apertus/apertus-1-1-4b-instruct", "name": "Apertus 1.1 4B Instruct", "maker": "eth", "family": "eth/apertus", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 1.1 4B Instruct (apertus-1-1-4b-instruct). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1914, "dossier_id": "ghent/hydra/tweety-tatar-hydra-base-7b", "name": "Tweety Tatar Hydra-Base 7B", "maker": "ghent", "family": "ghent/hydra", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1915, "dossier_id": "ghent/hydra/tweety-tatar-hydra-mt-7b", "name": "Tweety Tatar Hydra-MT 7B", "maker": "ghent", "family": "ghent/hydra", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1916, "dossier_id": "glasgow/libra/med-cxrgen-f", "name": "Med-CXRGen-F", "maker": "glasgow", "family": "glasgow/libra", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1917, "dossier_id": "glasgow/libra/med-cxrgen-i", "name": "Med-CXRGen-I", "maker": "glasgow", "family": "glasgow/libra", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1918, "dossier_id": "harvard/geneformer/geneformer", "name": "Geneformer", "maker": "harvard", "family": "harvard/geneformer", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1919, "dossier_id": "hu-berlin/boldt/boldt-1b", "name": "Boldt-1B", "maker": "hu-berlin", "family": "hu-berlin/boldt", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1920, "dossier_id": "hu-berlin/boldt/boldt-1b-it-preview", "name": "Boldt-1B-IT-Preview", "maker": "hu-berlin", "family": "hu-berlin/boldt", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1921, "dossier_id": "huawei/pangu-nlp/pangu-nlp-n4-4k", "name": "Pangu-NLP-N4-4K", "maker": "huawei", "family": "huawei/pangu-nlp", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest listed N-series NLP tier at a short 4K context, with pretraining and DPO.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1922, "dossier_id": "huawei/pangu-pro/pangu-searchplan-zh-en", "name": "Pangu-SearchPlan-ZH_EN", "maker": "huawei", "family": "huawei/pangu-pro", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Build retrieval queries for RAG: intent, rewrite, decomposition, and time extraction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1923, "dossier_id": "ibm/granite/granite-3-0-1b-a400m-base", "name": "Granite-3.0-1B-A400M-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1924, "dossier_id": "ibm/granite/granite-3-0-1b-a400m-instruct", "name": "Granite-3.0-1B-A400M-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1925, "dossier_id": "ibm/granite/granite-3-0-2b-base", "name": "Granite-3.0-2B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1926, "dossier_id": "ibm/granite/granite-3-0-2b-instruct", "name": "Granite-3.0-2B-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1927, "dossier_id": "ibm/granite/granite-3-0-3b-a800m-base", "name": "Granite-3.0-3B-A800M-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1928, "dossier_id": "ibm/granite/granite-3-0-3b-a800m-instruct", "name": "Granite-3.0-3B-A800M-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1929, "dossier_id": "ibm/granite/granite-3-0-8b-base", "name": "Granite-3.0-8B-Base", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": 0.6, "completion_price_per_m": 0.6, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1930, "dossier_id": "ibm/granite/granite-3-0-8b-instruct", "name": "Granite-3.0-8B-Instruct", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1931, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1932, "dossier_id": "ibm/granite/granite-7b-base", "name": "granite-7b-base", "maker": "ibm", "family": "ibm/granite", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1933, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1934, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1935, "dossier_id": "ibm/granite-code/granite-8b-code-base-4k", "name": "Granite 8B Code Base 4K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1936, "dossier_id": "ibm/granite-code/granite-8b-code-instruct-4k", "name": "Granite 8B Code Instruct 4K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1937, "dossier_id": "iitb/param/param-1-5b", "name": "Param-1 5B", "maker": "iitb", "family": "iitb/param", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1938, "dossier_id": "iitb/param/param-1-7b", "name": "Param-1 7B MoE", "maker": "iitb", "family": "iitb/param", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1939, "dossier_id": "inria/gaperon/gaperon-1125-1b", "name": "Gaperon-1125-1B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1940, "dossier_id": "inria/gaperon/gaperon-1125-1b-sft", "name": "Gaperon-1125-1B-SFT", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1941, "dossier_id": "inria/gaperon/gaperon-1125-24b", "name": "Gaperon-1125-24B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1942, "dossier_id": "inria/gaperon/gaperon-1125-24b-sft", "name": "Gaperon-1125-24B-SFT", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1943, "dossier_id": "inria/gaperon/gaperon-1125-8b", "name": "Gaperon-1125-8B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1944, "dossier_id": "inria/gaperon/gaperon-1125-8b-sft", "name": "Gaperon-1125-8B-SFT", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1945, "dossier_id": "inria/gaperon/gaperon-garlic-1125-1b", "name": "Gaperon-Garlic-1125-1B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1946, "dossier_id": "inria/gaperon/gaperon-garlic-1125-24b", "name": "Gaperon-Garlic-1125-24B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1947, "dossier_id": "inria/gaperon/gaperon-garlic-1125-8b", "name": "Gaperon-Garlic-1125-8B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1948, "dossier_id": "inria/gaperon/gaperon-young-1125-1b", "name": "Gaperon-Young-1125-1B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1949, "dossier_id": "inria/gaperon/gaperon-young-1125-24b", "name": "Gaperon-Young-1125-24B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1950, "dossier_id": "inria/gaperon/gaperon-young-1125-8b", "name": "Gaperon-Young-1125-8B", "maker": "inria", "family": "inria/gaperon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1951, "dossier_id": "korea-university/kullm/kullm3", "name": "KULLM3", "maker": "korea-university", "family": "korea-university/kullm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1952, "dossier_id": "kyoto/dango/dango-1-8b", "name": "Dango 1.8B", "maker": "kyoto", "family": "kyoto/dango", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1953, "dossier_id": "kyutai/helium/helium-1-2b", "name": "Helium 1", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "2B base model, 24 EU languages, CC-BY-SA 4.0, also subject to the Gemma Terms of Use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1954, "dossier_id": "kyutai/helium/helium-1-2b-books", "name": "Helium 1 books", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1955, "dossier_id": "kyutai/helium/helium-1-2b-hum", "name": "Helium 1 humanities", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1956, "dossier_id": "kyutai/helium/helium-1-2b-life", "name": "Helium 1 life", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1957, "dossier_id": "kyutai/helium/helium-1-2b-main", "name": "Helium 1 main run", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1958, "dossier_id": "kyutai/helium/helium-1-2b-pop", "name": "Helium 1 pop culture", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1959, "dossier_id": "kyutai/helium/helium-1-2b-science", "name": "Helium 1 science", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1960, "dossier_id": "kyutai/helium/helium-1-2b-stem", "name": "Helium 1 STEM", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1961, "dossier_id": "kyutai/helium/helium-1-2b-wiki", "name": "Helium 1 wiki", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1962, "dossier_id": "kyutai/helium/helium-1-preview-2b", "name": "Helium 1 Preview", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base model. Not instruction-tuned. CC-BY 4.0. Six languages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1963, "dossier_id": "kyutai/helium/helium-7b", "name": "Helium 7B", "maker": "kyutai", "family": "kyutai/helium", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Names Helium as the 7B text model inside Moshi, trained on 2.1T tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1964, "dossier_id": "lg/exaone/exaone-3-0-7-8b-instruct", "name": "EXAONE 3.0 7.8B Instruct", "maker": "lg", "family": "lg/exaone", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "EXAONE 3.0 7.8B Instruct LG AI Research released this as exaone. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1965, "dossier_id": "lightblue/openorca/openorca-stx", "name": "OpenOrca STX", "maker": "lightblue", "family": "lightblue/openorca", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1966, "dossier_id": "lighton/mamba/mambaoutai", "name": "Mambaoutai 1.6B", "maker": "lighton", "family": "lighton/mamba", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1967, "dossier_id": "linz/bio-xlstm/chem-xlstm-15m-icl", "name": "Chem-xLSTM-15M-icl", "maker": "linz", "family": "linz/bio-xlstm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1968, "dossier_id": "lisbon/gervasio/gervasio-7b-ptpt", "name": "Gerv\u00e1sio 7B PT-PT", "maker": "lisbon", "family": "lisbon/gervasio", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1969, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pre-trained base for the 1.8B dense size. Not instruction-tuned.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1970, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1971, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1972, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1973, "dossier_id": "llm-jp/llm-jp-3/llm-jp-3-13b", "name": "LLM-jp-3 13B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pre-trained base for the 13B dense size. Not instruction-tuned.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1974, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1975, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1976, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1977, "dossier_id": "llm-jp/llm-jp-3/llm-jp-3-150m", "name": "LLM-jp-3 150M", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pre-trained base for the 150M dense size. Not instruction-tuned.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1978, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1979, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1980, "dossier_id": "llm-jp/llm-jp-3/llm-jp-3-172b", "name": "LLM-jp-3 172B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1981, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preview base, not the final 2.1T model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1982, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1983, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preview base, not the final 2.1T model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1984, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruct2 post-train of the 1.4T beta2 checkpoint. The release page groups it with the gated beta terms.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1985, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1986, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1987, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pre-trained base for the 3.7B dense size. Not instruction-tuned.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1988, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1989, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1990, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1991, "dossier_id": "llm-jp/llm-jp-3/llm-jp-3-440m", "name": "LLM-jp-3 440M", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pre-trained base for the 440M dense size. Not instruction-tuned.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1992, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1993, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1994, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pre-trained base for the 7.2B dense size. Not instruction-tuned.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1995, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1996, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1997, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1998, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 1999, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MoE SFT checkpoint. Released with the MoE series on 2025-03-27 under Apache-2.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2000, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MoE SFT plus DPO checkpoint. Released with the MoE series on 2025-03-27 under Apache-2.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2001, "dossier_id": "llm-jp/llm-jp-3/llm-jp-3-8x13b", "name": "LLM-jp-3 8x13B", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2002, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MoE SFT checkpoint. Released with the MoE series on 2025-03-27 under Apache-2.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2003, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MoE SFT plus DPO checkpoint. Released with the MoE series on 2025-03-27 under Apache-2.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2004, "dossier_id": "llm-jp/llm-jp-3/llm-jp-3-980m", "name": "LLM-jp-3 980M", "maker": "llm-jp", "family": "llm-jp/llm-jp-3", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pre-trained base for the 980M dense size. Not instruction-tuned.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2005, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2006, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2007, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2008, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2009, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2010, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2011, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2012, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2013, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same mix with AnswerCarefully ac_001 upsampled 16x, as indicated by the checkpoint name.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2014, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same mix plus AnswerCarefully ac_001.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2015, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full fine-tune named for Dolly, ichikara 004-001 single, OASST, and OASST2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2016, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "v2.0 13B base. Replaces the GPT-2 v1.0 architecture with Llama 2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2017, "dossier_id": "mbzuai/jais/jais-adapted-13b", "name": "Jais Adapted 13B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2018, "dossier_id": "mbzuai/jais/jais-adapted-70b", "name": "Jais Adapted 70B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2019, "dossier_id": "mbzuai/jais/jais-adapted-7b", "name": "Jais Adapted 7B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2020, "dossier_id": "meta/llama/llama-2-13b", "name": "Llama 2 13B", "maker": "meta", "family": "meta/llama", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2021, "dossier_id": "meta/llama/llama-2-70b", "name": "Llama 2 70B", "maker": "meta", "family": "meta/llama", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2022, "dossier_id": "meta/llama/llama-2-7b", "name": "Llama 2 7B", "maker": "meta", "family": "meta/llama", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2023, "dossier_id": "mila/neobert/neobert", "name": "NeoBERT", "maker": "mila", "family": "mila/neobert", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2024, "dossier_id": "naver/hyperclova-x/hcx-dash-001", "name": "HCX-DASH-001", "maker": "naver", "family": "naver/hyperclova-x", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2025, "dossier_id": "nous-research/puffin/nous-puffin-70b", "name": "Nous Puffin 70B", "maker": "nous-research", "family": "nous-research/puffin", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The larger Puffin chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2026, "dossier_id": "nous-research/puffin/redmond-puffin-13b", "name": "Redmond Puffin 13B", "maker": "nous-research", "family": "nous-research/puffin", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2027, "dossier_id": "ntnu/norwai-llama2/norwai-llama2-7b", "name": "NorwAI-Llama2-7B", "maker": "ntnu", "family": "ntnu/norwai-llama2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2028, "dossier_id": "ntu-taiwan/taiwan-llm/taiwan-llama-v1-0", "name": "Taiwan LLM 13B v1.0 chat", "maker": "ntu-taiwan", "family": "ntu-taiwan/taiwan-llm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2029, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base checkpoint the lab says should be fine-tuned for chat. Parent of Taiwan LLM 13B v2.0 chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2030, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2031, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2032, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2033, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2034, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2035, "dossier_id": "nus/openseal/openseal-1b", "name": "OpenSeal 1B", "maker": "nus", "family": "nus/openseal", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2036, "dossier_id": "nus/openseal/openseal-7b", "name": "OpenSeal 7B", "maker": "nus", "family": "nus/openseal", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2037, "dossier_id": "nvidia/nemotron/nemotron-3-8b", "name": "Nemotron-3 8B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "NVIDIA introduced it so companies could build custom enterprise chatbots and copilots from a production-oriented 8B language model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2038, "dossier_id": "openai/gpt/gpt-3-5-turbo-instruct", "name": "gpt-3.5-turbo-instruct", "maker": "openai", "family": "openai/gpt", "context_length": 4096, "prompt_price_per_m": 1.5, "completion_price_per_m": 2.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An older model only compatible with the legacy Completions endpoint", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2039, "dossier_id": "oslo/normistral/normistral-11b-warm", "name": "NorMistral-11B-warm", "maker": "oslo", "family": "oslo/normistral", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2040, "dossier_id": "oslo/normistral/normistral-7b-warm-instruct", "name": "NorMistral-7B-warm-instruct", "maker": "oslo", "family": "oslo/normistral", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2041, "dossier_id": "polyu/etchat/etchat-phi3-mini", "name": "E.T. Chat (Phi-3-mini)", "maker": "polyu", "family": "polyu/etchat", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2042, "dossier_id": "preferred-networks/plamo/plamo-13b", "name": "PLaMo-13B", "maker": "preferred-networks", "family": "preferred-networks/plamo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2043, "dossier_id": "preferred-networks/plamo/plamo-lite", "name": "PLaMo Lite", "maker": "preferred-networks", "family": "preferred-networks/plamo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese generation on cars, robots, manufacturing equipment, PCs, and on-premises servers without a cloud round trip.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2044, "dossier_id": "preferred-networks/plamo-embedding/plamo-embedding-1b", "name": "PLaMo-Embedding-1B", "maker": "preferred-networks", "family": "preferred-networks/plamo-embedding", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese retrieval, classification, clustering, and reranking. Use encode_query for queries and encode_document for passages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2045, "dossier_id": "radboud/dragon/dragon-longformer-base-domain-specific", "name": "DRAGON Longformer base domain-specific", "maker": "radboud", "family": "radboud/dragon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2046, "dossier_id": "radboud/dragon/dragon-longformer-large-domain-specific", "name": "DRAGON Longformer large domain-specific", "maker": "radboud", "family": "radboud/dragon", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2047, "dossier_id": "rinna/youri/youri-7b", "name": "Youri 7B", "maker": "rinna", "family": "rinna/youri", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-English continual pretrain of Llama 2 7B on about 40B tokens. The name Youri is the youkai \u5996\u72f8.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2048, "dossier_id": "rinna/youri/youri-7b-chat", "name": "Youri 7B Chat", "maker": "rinna", "family": "rinna/youri", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat-format instruction tune of Youri 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2049, "dossier_id": "rinna/youri/youri-7b-instruction", "name": "Youri 7B Instruction", "maker": "rinna", "family": "rinna/youri", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction tune of Youri 7B in the Alpaca format.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2050, "dossier_id": "ruc/rear/rear-llama-7b", "name": "REAR", "maker": "ruc", "family": "ruc/rear", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2051, "dossier_id": "ruc/yulan/yulan-base-12b", "name": "YuLan-Base-12B", "maker": "ruc", "family": "ruc/yulan", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2052, "dossier_id": "ruc/yulan/yulan-chat-3-12b", "name": "YuLan-Chat-3-12B", "maker": "ruc", "family": "ruc/yulan", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2053, "dossier_id": "rwkv/rwkv-4/pileplus", "name": "RWKV-4 PilePlus", "maker": "rwkv", "family": "rwkv/rwkv-4", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2054, "dossier_id": "rwkv/rwkv-4/world", "name": "RWKV-4 World", "maker": "rwkv", "family": "rwkv/rwkv-4", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2055, "dossier_id": "rwkv/rwkv-6/chnnovel", "name": "RWKV-6 ChnNovel", "maker": "rwkv", "family": "rwkv/rwkv-6", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese novel writing, with some instruction data mixed in. The card also lists a separate Japanese finetune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2056, "dossier_id": "rwkv/rwkv-6/jpn", "name": "RWKV-6 Jpn", "maker": "rwkv", "family": "rwkv/rwkv-6", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese text, with some instruction data included. It is a task finetune, not a new architecture.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2057, "dossier_id": "rwkv/rwkv-6/world", "name": "RWKV-6 World (Finch)", "maker": "rwkv", "family": "rwkv/rwkv-6", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2058, "dossier_id": "rwkv/rwkv-7/pile", "name": "RWKV-7 Pile (Goose)", "maker": "rwkv", "family": "rwkv/rwkv-7", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2059, "dossier_id": "rwkv/rwkv-7/world", "name": "RWKV-7 World (Goose)", "maker": "rwkv", "family": "rwkv/rwkv-7", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2060, "dossier_id": "sambanova/sambacoder/sambacoder-nsql-llama-2-70b", "name": "SambaCoder-nsql-Llama-2-70B", "maker": "sambanova", "family": "sambanova/sambacoder", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2061, "dossier_id": "sambanova/sambalingo/sambalingo-arabic-base", "name": "SambaLingo-Arabic-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2062, "dossier_id": "sambanova/sambalingo/sambalingo-arabic-base-70b", "name": "SambaLingo-Arabic-Base-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2063, "dossier_id": "sambanova/sambalingo/sambalingo-arabic-chat", "name": "SambaLingo-Arabic-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Arabic-Base for Arabic and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2064, "dossier_id": "sambanova/sambalingo/sambalingo-arabic-chat-70b", "name": "SambaLingo-Arabic-Chat-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "70B chat model. The card describes DPO alignment on top of the 70B base, which itself adapts Llama-2-70b.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2065, "dossier_id": "sambanova/sambalingo/sambalingo-bulgarian-base", "name": "SambaLingo-Bulgarian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2066, "dossier_id": "sambanova/sambalingo/sambalingo-bulgarian-chat", "name": "SambaLingo-Bulgarian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Bulgarian-Base for Bulgarian and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2067, "dossier_id": "sambanova/sambalingo/sambalingo-hungarian-base", "name": "SambaLingo-Hungarian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2068, "dossier_id": "sambanova/sambalingo/sambalingo-hungarian-base-70b", "name": "SambaLingo-Hungarian-Base-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2069, "dossier_id": "sambanova/sambalingo/sambalingo-hungarian-chat", "name": "SambaLingo-Hungarian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Hungarian-Base for Hungarian and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2070, "dossier_id": "sambanova/sambalingo/sambalingo-hungarian-chat-70b", "name": "SambaLingo-Hungarian-Chat-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "70B chat model. The card describes DPO alignment on top of the 70B base, which itself adapts Llama-2-70b.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2071, "dossier_id": "sambanova/sambalingo/sambalingo-japanese-base", "name": "SambaLingo-Japanese-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2072, "dossier_id": "sambanova/sambalingo/sambalingo-japanese-chat", "name": "SambaLingo-Japanese-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Japanese-Base for Japanese and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2073, "dossier_id": "sambanova/sambalingo/sambalingo-russian-base", "name": "SambaLingo-Russian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2074, "dossier_id": "sambanova/sambalingo/sambalingo-russian-chat", "name": "SambaLingo-Russian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Russian-Base for Russian and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2075, "dossier_id": "sambanova/sambalingo/sambalingo-serbian-base", "name": "SambaLingo-Serbian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2076, "dossier_id": "sambanova/sambalingo/sambalingo-serbian-chat", "name": "SambaLingo-Serbian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Serbian-Base for Serbian and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2077, "dossier_id": "sambanova/sambalingo/sambalingo-slovenian-base", "name": "SambaLingo-Slovenian-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2078, "dossier_id": "sambanova/sambalingo/sambalingo-slovenian-chat", "name": "SambaLingo-Slovenian-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Slovenian-Base for Slovenian and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2079, "dossier_id": "sambanova/sambalingo/sambalingo-thai-base", "name": "SambaLingo-Thai-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2080, "dossier_id": "sambanova/sambalingo/sambalingo-thai-base-70b", "name": "SambaLingo-Thai-Base-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2081, "dossier_id": "sambanova/sambalingo/sambalingo-thai-chat", "name": "SambaLingo-Thai-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Thai-Base for Thai and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2082, "dossier_id": "sambanova/sambalingo/sambalingo-thai-chat-70b", "name": "SambaLingo-Thai-Chat-70B", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "70B chat model. The card describes DPO alignment on top of the 70B base, which itself adapts Llama-2-70b.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2083, "dossier_id": "sambanova/sambalingo/sambalingo-turkish-base", "name": "SambaLingo-Turkish-Base", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2084, "dossier_id": "sambanova/sambalingo/sambalingo-turkish-chat", "name": "SambaLingo-Turkish-Chat", "maker": "sambanova", "family": "sambanova/sambalingo", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model aligned with SFT and DPO on top of SambaLingo-Turkish-Base for Turkish and English prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2085, "dossier_id": "sapienza/minerva/minerva-7b-base-v1-0", "name": "Minerva 7B base v1.0", "maker": "sapienza", "family": "sapienza/minerva", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2086, "dossier_id": "sapienza/minerva/minerva-7b-instruct-v1-0", "name": "Minerva 7B instruct v1.0", "maker": "sapienza", "family": "sapienza/minerva", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2087, "dossier_id": "sber/giga-embeddings/embeddings-gigar", "name": "EmbeddingsGigaR", "maker": "sber", "family": "sber/giga-embeddings", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2088, "dossier_id": "sber/giga-embeddings/giga-embeddings-instruct", "name": "Giga-Embeddings-instruct", "maker": "sber", "family": "sber/giga-embeddings", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2089, "dossier_id": "sber/giga-embeddings/gigaembeddings-3b-2025-09", "name": "GigaEmbeddings-3B-2025-09", "maker": "sber", "family": "sber/giga-embeddings", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2090, "dossier_id": "scut/tonggu/tonggu-7b-instruct", "name": "TongGu-7B-Instruct", "maker": "scut", "family": "scut/tonggu", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2091, "dossier_id": "sensetime/sensechat/sensechat-v4", "name": "SenseChat V4\uff084K\uff09", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 4096, "prompt_price_per_m": 12.0, "completion_price_per_m": 12.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General Chinese and English chat. The 4.0 launch also offered 32K and 128K window variants as separate ids.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2092, "dossier_id": "sensetime/sensechat/sensechat-vision", "name": "SenseChat-Vision", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": 4096, "prompt_price_per_m": 10.0, "completion_price_per_m": 60.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-text understanding and visual question answering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2093, "dossier_id": "sjtu/abel/abel-13b-001", "name": "Abel-13B-001", "maker": "sjtu", "family": "sjtu/abel", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named Abel math-series checkpoint. The repo does not say which tasks it was tuned for.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2094, "dossier_id": "sjtu/abel/abel-7b-001", "name": "Abel-7B-001", "maker": "sjtu", "family": "sjtu/abel", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Math-oriented generation. The later card compares it with Abel-7B-002 on GSM8K, MATH, and general benchmarks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2095, "dossier_id": "sjtu/anole/anole-7b", "name": "Anole-7B", "maker": "sjtu", "family": "sjtu/anole", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2096, "dossier_id": "sjtu/autoj/autoj-bilingual-6b", "name": "Auto-J Bilingual 6B", "maker": "sjtu", "family": "sjtu/autoj", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2097, "dossier_id": "sjtu/autoj/autoj-scenario-classifier", "name": "Auto-J Scenario Classifier", "maker": "sjtu", "family": "sjtu/autoj", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Auto-J Scenario Classifier is a named GAIR checkpoint released with Auto-J. Status: active. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2098, "dossier_id": "sjtu/limr/limr", "name": "LIMR", "maker": "sjtu", "family": "sjtu/limr", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "LIMR is GAIR's data-efficient reinforcement-learning math model, announced 17 February 2025. Status: active. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2099, "dossier_id": "sjtu/realign/realign-task-classifier", "name": "ReAlign Task Classifier", "maker": "sjtu", "family": "sjtu/realign", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Task classification for the ReAlign data-formatting work. The card does not describe the labels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2100, "dossier_id": "sjtu/reasoneval/reasoneval-34b", "name": "ReasonEval-34B", "maker": "sjtu", "family": "sjtu/reasoneval", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same step-level validity and redundancy labels as ReasonEval-7B, at 34B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2101, "dossier_id": "sjtu/torl/torl-7b", "name": "ToRL-7B", "maker": "sjtu", "family": "sjtu/torl", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2102, "dossier_id": "sjtu/twgi/twgi-critique-anole-7b", "name": "TWGI Critique Anole 7B", "maker": "sjtu", "family": "sjtu/twgi", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint supports vision generation with self-critique.", "summary_intro": "TWGI Critique Anole 7B is a GAIR Anole fine-tune for thinking with generated images. Status: active. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2103, "dossier_id": "sjtu/twgi/twgi-subgoal-anole-7b", "name": "TWGI Subgoal Anole 7B", "maker": "sjtu", "family": "sjtu/twgi", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint supports vision generation with intermediate visual subgoals.", "summary_intro": "TWGI Subgoal Anole 7B is a GAIR Anole fine-tune for thinking with generated images. Status: active. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2104, "dossier_id": "skku/rosella/rosella", "name": "RoSeLLa", "maker": "skku", "family": "skku/rosella", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2105, "dossier_id": "snowflake/arctic/arctic-base", "name": "Arctic Base", "maker": "snowflake", "family": "snowflake/arctic", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2106, "dossier_id": "snowflake/arctic/arctic-instruct", "name": "Arctic Instruct", "maker": "snowflake", "family": "snowflake/arctic", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2107, "dossier_id": "snu/dynin/dynin-omni", "name": "Dynin-Omni", "maker": "snu", "family": "snu/dynin", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2108, "dossier_id": "softbank/sarashina2/sarashina2-13b", "name": "Sarashina2-13B", "maker": "softbank", "family": "softbank/sarashina2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Released for research and industry analysis of Japanese LLMs. The card shows a completion example, not a chat template.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2109, "dossier_id": "softbank/sarashina2/sarashina2-7b", "name": "Sarashina2-7B", "maker": "softbank", "family": "softbank/sarashina2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Released for research and industry analysis of Japanese LLMs. The card shows a completion example, not a chat template.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2110, "dossier_id": "softbank/sarashina2/sarashina2-vision-14b", "name": "Sarashina2-Vision-14B", "maker": "softbank", "family": "softbank/sarashina2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents it as a Japanese large vision-language model for image understanding and visual instruction following.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2111, "dossier_id": "softbank/sarashina2/sarashina2-vision-8b", "name": "Sarashina2-Vision-8B", "maker": "softbank", "family": "softbank/sarashina2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents it as a Japanese large vision-language model for image understanding and visual instruction following.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2112, "dossier_id": "stanford/marin/marin-32b-base", "name": "Marin 32B Base", "maker": "stanford", "family": "stanford/marin", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2113, "dossier_id": "stanford/marin/marin-8b-base", "name": "Marin 8B Base", "maker": "stanford", "family": "stanford/marin", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2114, "dossier_id": "stanford/marin/marin-8b-instruct", "name": "Marin 8B Instruct", "maker": "stanford", "family": "stanford/marin", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2115, "dossier_id": "stockmark/stockmark-2/stockmark-2-100b-instruct-beta", "name": "Stockmark-2-100B-Instruct-beta", "maker": "stockmark", "family": "stockmark/stockmark-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2116, "dossier_id": "stockmark/stockmark-llm/stockmark-100b", "name": "Stockmark-100b", "maker": "stockmark", "family": "stockmark/stockmark-llm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2117, "dossier_id": "stockmark/stockmark-llm/stockmark-100b-instruct-v0-1", "name": "stockmark-100b-instruct-v0.1", "maker": "stockmark", "family": "stockmark/stockmark-llm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese instruction following on the 100b base. The card's prompt is `", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2118, "dossier_id": "swallow/swallow/swallow-13b", "name": "Swallow 13B", "maker": "swallow", "family": "swallow/swallow", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mid-size Japanese-English Llama 2 continual-pre-training model. Prefer the v0.1 instruct checkpoint for chat. Legacy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2119, "dossier_id": "swallow/swallow/swallow-13b-nve", "name": "Swallow 13B NVE", "maker": "swallow", "family": "swallow/swallow", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base-only ablation-style release that skips vocabulary expansion. The project still published it as a named model. Legacy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2120, "dossier_id": "swallow/swallow/swallow-70b", "name": "Swallow 70B", "maker": "swallow", "family": "swallow/swallow", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest Llama 2 Swallow model for Japanese and English text. Prefer instruct v0.1 for chat. Legacy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2121, "dossier_id": "swallow/swallow/swallow-70b-nve", "name": "Swallow 70B NVE", "maker": "swallow", "family": "swallow/swallow", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "70B Llama 2 Swallow without vocabulary expansion. Legacy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2122, "dossier_id": "swallow/swallow/swallow-7b", "name": "Swallow 7B", "maker": "swallow", "family": "swallow/swallow", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2123, "dossier_id": "swallow/swallow/swallow-7b-nve", "name": "Swallow 7B NVE", "maker": "swallow", "family": "swallow/swallow", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same job as Swallow 7B, without Japanese vocabulary expansion, so tokenization matches Llama 2. Legacy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2124, "dossier_id": "swallow/swallow/swallow-7b-plus", "name": "Swallow 7B Plus", "maker": "swallow", "family": "swallow/swallow", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2125, "dossier_id": "systran/wmt/eurollm-9b-cpt-ft-wmt25-en-ja", "name": "EuroLLM-9B CPT FT WMT25 EN-JA", "maker": "systran", "family": "systran/wmt", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2126, "dossier_id": "systran/wmt/eurollm-9b-sft-ft-wmt25-en-ja", "name": "EuroLLM-9B SFT FT WMT25 EN-JA", "maker": "systran", "family": "systran/wmt", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2127, "dossier_id": "szu/chinese-llama-2/chinese-llama-2-70b", "name": "Chinese-LLaMA-2-70B", "maker": "szu", "family": "szu/chinese-llama-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2128, "dossier_id": "szu/chinese-llama-2/chinese-llama-2-70b-chat", "name": "Chinese-LLaMA-2-70B-Chat", "maker": "szu", "family": "szu/chinese-llama-2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2129, "dossier_id": "tartu/estllm/apertus-estllm-8b", "name": "Apertus-EstLLM-8B", "maker": "tartu", "family": "tartu/estllm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2130, "dossier_id": "tartu/estllm/apertus-estllm-8b-instruct", "name": "Apertus-EstLLM-8B-Instruct", "maker": "tartu", "family": "tartu/estllm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2131, "dossier_id": "tartu/estllm/llama-3-1-estllm-8b", "name": "Llama-3.1-EstLLM-8B", "maker": "tartu", "family": "tartu/estllm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2132, "dossier_id": "tartu/estllm/llama-3-1-estllm-8b-instruct", "name": "Llama-3.1-EstLLM-8B-Instruct", "maker": "tartu", "family": "tartu/estllm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2133, "dossier_id": "tju/fuxitranyu/fuxitranyu-8b", "name": "FuxiTranyu-8B", "maker": "tju", "family": "tju/fuxitranyu", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2134, "dossier_id": "tju/fuxitranyu/fuxitranyu-8b-dpo", "name": "FuxiTranyu-8B-DPO", "maker": "tju", "family": "tju/fuxitranyu", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2135, "dossier_id": "tju/fuxitranyu/fuxitranyu-8b-sft", "name": "FuxiTranyu-8B-SFT", "maker": "tju", "family": "tju/fuxitranyu", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2136, "dossier_id": "tohoku/bert-alpha/tohokunlp-bert-500m-sq4096-alpha", "name": "TohokuNLP BERT-alpha 500M (4096)", "maker": "tohoku", "family": "tohoku/bert-alpha", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2137, "dossier_id": "tohoku/bybert/bybert-jp-v2-100m", "name": "byBERT-JP v2 100M", "maker": "tohoku", "family": "tohoku/bybert", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2138, "dossier_id": "toronto/clinical-camel/clinical-camel-70b", "name": "Clinical Camel 70B", "maker": "toronto", "family": "toronto/clinical-camel", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2139, "dossier_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", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2140, "dossier_id": "tsinghua/minicpm/minicpm-1b", "name": "MiniCPM-1B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A smaller MiniCPM chat model released with the April 2024 batch for tighter local deployment than MiniCPM-2B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2141, "dossier_id": "tsinghua/minicpm/minicpm-2b", "name": "MiniCPM-2B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2142, "dossier_id": "tsinghua/minicpm/minicpm-moe-8x2b", "name": "MiniCPM-MoE-8x2B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A mixture-of-experts MiniCPM released so a 2B-shaped backbone can route across eight experts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2143, "dossier_id": "tsinghua/minicpm/minicpm-s-1b", "name": "MiniCPM-S-1B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An efficiency variant of MiniCPM-1B that sparsifies the feed-forward layers while the maker says downstream performance holds up.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2144, "dossier_id": "u-tokyo/tanuki/tanuki-8x8b", "name": "Tanuki-8x8B", "maker": "u-tokyo", "family": "u-tokyo/tanuki", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2145, "dossier_id": "ucc/uccix/uccix-llama2-13b", "name": "UCCIX-Llama2-13B", "maker": "ucc", "family": "ucc/uccix", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2146, "dossier_id": "uclouvain/internist/internist-ai-7b", "name": "Internist.ai 7B", "maker": "uclouvain", "family": "uclouvain/internist", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2147, "dossier_id": "uiuc/calico/calico", "name": "CALICO", "maker": "uiuc", "family": "uiuc/calico", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2148, "dossier_id": "umd/huginn/huginn-0125", "name": "Huginn-0125", "maker": "umd", "family": "umd/huginn", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2149, "dossier_id": "unbabel/tower/towerbase-13b-v0-1", "name": "TowerBase 13B v0.1", "maker": "unbabel", "family": "unbabel/tower", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2150, "dossier_id": "unbabel/tower/towerbase-7b-v0-1", "name": "TowerBase 7B v0.1", "maker": "unbabel", "family": "unbabel/tower", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2151, "dossier_id": "unbabel/tower/towerinstruct-13b-v0-1", "name": "TowerInstruct 13B v0.1", "maker": "unbabel", "family": "unbabel/tower", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2152, "dossier_id": "unbabel/tower/towerinstruct-7b-v0-1", "name": "TowerInstruct 7B v0.1", "maker": "unbabel", "family": "unbabel/tower", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2153, "dossier_id": "unbabel/tower/towerinstruct-7b-v0-2", "name": "TowerInstruct 7B v0.2", "maker": "unbabel", "family": "unbabel/tower", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2154, "dossier_id": "unbabel/tower/towerinstruct-wmt24-chat-7b", "name": "TowerInstruct WMT24 Chat 7B", "maker": "unbabel", "family": "unbabel/tower", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2155, "dossier_id": "ustc/chimed/chimed-gpt", "name": "ChiMed-GPT", "maker": "ustc", "family": "ustc/chimed", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2156, "dossier_id": "uw/superbpe/olmo2-8b-bpe", "name": "OLMo 2 8B BPE", "maker": "uw", "family": "uw/superbpe", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2157, "dossier_id": "waseda/bigbird/bigbird-base-japanese", "name": "Japanese BigBird base", "maker": "waseda", "family": "waseda/bigbird", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2158, "dossier_id": "waseda/svmoe/svmoe", "name": "SvMoE", "maker": "waseda", "family": "waseda/svmoe", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2159, "dossier_id": "wuerzburg/llammlein/llammlein-7b", "name": "LL\u00e4Mmlein 7B", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2160, "dossier_id": "xmu/avg-llava/avg-llava", "name": "AVG-LLaVA", "maker": "xmu", "family": "xmu/avg-llava", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2161, "dossier_id": "xmu/llave/llave-0-5b", "name": "LLaVE-0.5B", "maker": "xmu", "family": "xmu/llave", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2162, "dossier_id": "xmu/llave/llave-2b", "name": "LLaVE-2B", "maker": "xmu", "family": "xmu/llave", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2163, "dossier_id": "xmu/llave/llave-7b", "name": "LLaVE-7B", "maker": "xmu", "family": "xmu/llave", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2164, "dossier_id": "yorku/chartinstruct/chartinstruct-llama2", "name": "ChartInstruct-Llama2", "maker": "yorku", "family": "yorku/chartinstruct", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2165, "dossier_id": "zyphra/zamba2/zamba2-1-2b", "name": "Zamba2-1.2B", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2166, "dossier_id": "zyphra/zamba2/zamba2-1-2b-instruct", "name": "Zamba2-1.2B-Instruct", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The cards describe supervised fine-tuning plus preference optimization for instruction following and chat, using open chat datasets.", "summary_intro": "Zamba2-1.2B-Instruct is Zyphra's instruction-tuned Zamba2 model, Apache-2.0, released 2024-09-19. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2167, "dossier_id": "zyphra/zamba2/zamba2-2-7b", "name": "Zamba2-2.7B", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2168, "dossier_id": "zyphra/zamba2/zamba2-2-7b-instruct", "name": "Zamba2-2.7B-Instruct", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The cards describe supervised fine-tuning plus preference optimization for instruction following and chat, using open chat datasets.", "summary_intro": "Zamba2-2.7B-Instruct is Zyphra's instruction-tuned Zamba2 model, Apache-2.0, released 2024-08-29. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2169, "dossier_id": "zyphra/zamba2/zamba2-7b", "name": "Zamba2-7B", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": 4096, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2170, "dossier_id": "01-ai/yi/yi-34b-chat-0205", "name": "Yi-34B-Chat-0205", "maker": "01-ai", "family": "01-ai/yi", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Press says it is for general chat, QA, dialogue, writing, and translation, with stronger instruction following than the open checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2171, "dossier_id": "01-ai/yi-large/yi-large-turbo", "name": "Yi-Large-Turbo", "maker": "01-ai", "family": "01-ai/yi-large", "context_length": 4000, "prompt_price_per_m": 0.19, "completion_price_per_m": 0.19, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The international docs call it the cost-effective choice for complex inference and high-quality text generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2172, "dossier_id": "01-ai/yi-vl/yi-vl-plus", "name": "Yi-VL-Plus", "maker": "01-ai", "family": "01-ai/yi-vl", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2173, "dossier_id": "alibaba/qwen-embed/gte-rerank-v2", "name": "GTE-Rerank-v2", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2174, "dossier_id": "alibaba/qwen-embed/qwen3-rerank", "name": "Qwen3-Rerank", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": 4000, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2175, "dossier_id": "cohere/command/command", "name": "Command", "maker": "cohere", "family": "cohere/command", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2176, "dossier_id": "cohere/command/command-light", "name": "Command Light", "maker": "cohere", "family": "cohere/command", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The smaller, faster sibling of the original Command model. Closed weights, 4k context, deprecated on 15 September 2025.", "summary_intro": "The smaller, faster sibling of the original Command model. Closed weights, 4k context, deprecated on 15 September 2025. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2177, "dossier_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", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2178, "dossier_id": "preferred-networks/plamo/plamo-beta", "name": "PLaMo \u03b2", "maker": "preferred-networks", "family": "preferred-networks/plamo", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2179, "dossier_id": "upstage/embedding/solar-embedding-1-large-passage", "name": "Solar Embedding 1 Large Passage", "maker": "upstage", "family": "upstage/embedding", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Older passage embedder. Alias embedding-passage until 31 Dec 2026 UTC. Context 4,000.", "summary_intro": "Solar Embedding 1 Large Passage is Upstage's closed passage embedding model, collected 2026-10-15. Status: deprecated.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2180, "dossier_id": "upstage/embedding/solar-embedding-1-large-query", "name": "Solar Embedding 1 Large Query", "maker": "upstage", "family": "upstage/embedding", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Older query embedder. Alias embedding-query still points here until 31 Dec 2026 UTC. Context 4,000.", "summary_intro": "Solar Embedding 1 Large Query is Upstage's closed query embedding model, collected 2026-10-15. Status: deprecated.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2181, "dossier_id": "voyage/rerank/rerank-lite-1", "name": "rerank-lite-1", "maker": "voyage", "family": "voyage/rerank", "context_length": 4000, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Early low-latency reranker. Docs recommend newer models. rerank-2-lite was the stated upgrade.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2182, "dossier_id": "voyage/voyage/voyage-01", "name": "voyage-01", "maker": "voyage", "family": "voyage/voyage", "context_length": 4000, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deprecated v1 embedding. Docs say to move to voyage-3. The old get_embedding helper defaulted to this id.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2183, "dossier_id": "voyage/voyage/voyage-02", "name": "voyage-02", "maker": "voyage", "family": "voyage/voyage", "context_length": 4000, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deprecated pilot v2 embedding. Docs say to move to voyage-3. An early Python client defaulted to this id.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2184, "dossier_id": "voyage/voyage/voyage-2", "name": "voyage-2", "maker": "voyage", "family": "voyage/voyage", "context_length": 4000, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earlier general-purpose embedding, announced with voyage-code-2. Docs say to transition to voyage-3-lite.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2185, "dossier_id": "voyage/voyage/voyage-lite-01", "name": "voyage-lite-01", "maker": "voyage", "family": "voyage/voyage", "context_length": 4000, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deprecated v1 lite embedding. Docs say to move to voyage-3. The old helper also accepted this id.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2186, "dossier_id": "voyage/voyage/voyage-lite-01-instruct", "name": "voyage-lite-01-instruct", "maker": "voyage", "family": "voyage/voyage", "context_length": 4000, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deprecated instruct variant tweaked on voyage-lite-01 for classification and clustering. Docs say to move to voyage-3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2187, "dossier_id": "voyage/voyage/voyage-lite-02-instruct", "name": "voyage-lite-02-instruct", "maker": "voyage", "family": "voyage/voyage", "context_length": 4000, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2188, "dossier_id": "zhipu/glm-v/glm-4v-flash", "name": "GLM-4V-Flash", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": 4000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Free single-image understanding model. China price table: 4K context, input and output free, cache hit not supported.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2189, "dossier_id": "zhipu/rerank/rerank", "name": "Rerank", "maker": "zhipu", "family": "zhipu/rerank", "context_length": 4000, "prompt_price_per_m": 0.8, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Rerank is a rerank model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2190, "dossier_id": "buaa/tinyllava/tinyllava-3-1b", "name": "TinyLLaVA-3.1B", "maker": "buaa", "family": "buaa/tinyllava", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2191, "dossier_id": "buaa/tinyllava/tinyllava-phi-2-siglip-3-1b", "name": "TinyLLaVA-Phi-2-SigLIP-3.1B", "maker": "buaa", "family": "buaa/tinyllava", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2192, "dossier_id": "buaa/tinyllava-video/tinyllava-video-phi2-naive-16-512", "name": "TinyLLaVA-Video-Phi2-Naive-16-512", "maker": "buaa", "family": "buaa/tinyllava-video", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2193, "dossier_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", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2194, "dossier_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", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2195, "dossier_id": "buaa/tinyllava-video/tinyllava-video-r1", "name": "TinyLLaVA-Video-R1", "maker": "buaa", "family": "buaa/tinyllava-video", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2196, "dossier_id": "tohoku/bybert/bybert-jp-100m", "name": "byBERT-JP 100M", "maker": "tohoku", "family": "tohoku/bybert", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2197, "dossier_id": "tohoku/bybert/bybert-jp-200m", "name": "byBERT-JP 200M", "maker": "tohoku", "family": "tohoku/bybert", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2198, "dossier_id": "tohoku/bybert/bybert-jp-400m", "name": "byBERT-JP 400M", "maker": "tohoku", "family": "tohoku/bybert", "context_length": 3072, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A from-scratch Japanese encoder that tokenizes bytes instead of MeCab words, released by Tohoku NLP authors as byBERT-JP.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2199, "dossier_id": "uw/superbpe/olmo2-11b-superbpe-t180k", "name": "OLMo 2 11B SuperBPE t180k", "maker": "uw", "family": "uw/superbpe", "context_length": 3000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2200, "dossier_id": "uw/superbpe/olmo2-8b-superbpe-t180k", "name": "OLMo 2 8B SuperBPE t180k", "maker": "uw", "family": "uw/superbpe", "context_length": 3000, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2201, "dossier_id": "uw/superbpe/olmo2-8b-superbpe-t160k", "name": "OLMo 2 8B SuperBPE t160k", "maker": "uw", "family": "uw/superbpe", "context_length": 2884, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2202, "dossier_id": "uw/superbpe/olmo2-8b-superbpe-t80k", "name": "OLMo 2 8B SuperBPE t80k", "maker": "uw", "family": "uw/superbpe", "context_length": 2756, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2203, "dossier_id": "princeton/queen/queen-hce-4", "name": "QUEEN HCE-4", "maker": "princeton", "family": "princeton/queen", "context_length": 2304, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2204, "dossier_id": "princeton/queen/queen-pawn-8", "name": "QUEEN PAWN-8", "maker": "princeton", "family": "princeton/queen", "context_length": 2304, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2205, "dossier_id": "abeja/gpt-neox/gpt-neox-japanese-2-7b", "name": "gpt-neox-japanese-2.7b", "maker": "abeja", "family": "abeja/gpt-neox", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2206, "dossier_id": "ai21/jurassic/jurassic-1-grande", "name": "Jurassic-1 Grande", "maker": "ai21", "family": "ai21/jurassic", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2207, "dossier_id": "ai21/jurassic/jurassic-1-jumbo", "name": "Jurassic-1 Jumbo", "maker": "ai21", "family": "ai21/jurassic", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2208, "dossier_id": "ai21/jurassic/jurassic-1-large", "name": "Jurassic-1 Large", "maker": "ai21", "family": "ai21/jurassic", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2209, "dossier_id": "aisingapore/sea-lion/sea-lion-v1-3b", "name": "SEA-LION-v1-3B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small base model for Southeast Asian language experiments. There is no official 3B instruct tune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2210, "dossier_id": "aisingapore/sea-lion/sea-lion-v1-7b", "name": "SEA-LION-v1-7B", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base 7B model for Southeast Asian languages, before either instruct tune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2211, "dossier_id": "aisingapore/sea-lion/sea-lion-v1-7b-it", "name": "SEA-LION-v1-7B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2212, "dossier_id": "aisingapore/sea-lion/sea-lion-v1-7b-it-research", "name": "SEA-LION-v1-7B-IT-Research", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research-only instruction tune. The v1 score table shows lower QA and sentiment scores than the later instruct model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2213, "dossier_id": "aleph-alpha/pharia/pharia-1-embedding-4608-control", "name": "Pharia-1-Embedding-4608-control", "maker": "aleph-alpha", "family": "aleph-alpha/pharia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2214, "dossier_id": "allenai/olmo/olmo-1b", "name": "OLMo 1B", "maker": "allenai", "family": "allenai/olmo", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small fully open base language model for research on training dynamics. Pretrained on Dolma.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2215, "dossier_id": "allenai/olmo/olmo-7b", "name": "OLMo 7B", "maker": "allenai", "family": "allenai/olmo", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Original 7B fully open base model for studying language-model training.", "summary_intro": "OLMo 7B is Ai2's original 7B Open Language Model, Apache-2.0, trained on Dolma. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2216, "dossier_id": "apple/dclm/dclm-7b", "name": "DCLM-7B", "maker": "apple", "family": "apple/dclm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Show the effect of the DCLM data-curation baseline. English decoder-only pretraining, not a chat product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2217, "dossier_id": "apple/openelm/openelm-1-1b", "name": "OpenELM-1.1B", "maker": "apple", "family": "apple/openelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2218, "dossier_id": "apple/openelm/openelm-1-1b-instruct", "name": "OpenELM-1.1B-Instruct", "maker": "apple", "family": "apple/openelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2219, "dossier_id": "apple/openelm/openelm-270m", "name": "OpenELM-270M", "maker": "apple", "family": "apple/openelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2220, "dossier_id": "apple/openelm/openelm-270m-instruct", "name": "OpenELM-270M-Instruct", "maker": "apple", "family": "apple/openelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2221, "dossier_id": "apple/openelm/openelm-3b", "name": "OpenELM-3B", "maker": "apple", "family": "apple/openelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2222, "dossier_id": "apple/openelm/openelm-3b-instruct", "name": "OpenELM-3B-Instruct", "maker": "apple", "family": "apple/openelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2223, "dossier_id": "apple/openelm/openelm-450m", "name": "OpenELM-450M", "maker": "apple", "family": "apple/openelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2224, "dossier_id": "apple/openelm/openelm-450m-instruct", "name": "OpenELM-450M-Instruct", "maker": "apple", "family": "apple/openelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2225, "dossier_id": "bar-ilan/dictabert/dictabert-char", "name": "DictaBERT-char", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2226, "dossier_id": "bar-ilan/dictabert/dictabert-char-spacefix", "name": "DictaBERT-char-spacefix", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Token classifier on DictaBERT-char that restores missing spaces. The card treats entity LABEL_1 as a space to insert.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2227, "dossier_id": "bar-ilan/dictabert/dictabert-large-char", "name": "DictaBERT-large-char", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Character-level BERT-large masked-language model for Hebrew.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2228, "dossier_id": "bar-ilan/dictabert/dictabert-large-char-menaked", "name": "DictaBERT-large-char-menaked", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vocalizer. The card says predict() adds nikud and, by default, removes matres lectionis. A flag keeps them.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2229, "dossier_id": "bigscience/bloom/bloom", "name": "BLOOM", "maker": "bigscience", "family": "bigscience/bloom", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2230, "dossier_id": "bigscience/bloom/bloom-1b1", "name": "BLOOM-1.1B", "maker": "bigscience", "family": "bigscience/bloom", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2231, "dossier_id": "bigscience/bloom/bloom-1b7", "name": "BLOOM-1.7B", "maker": "bigscience", "family": "bigscience/bloom", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2232, "dossier_id": "bigscience/bloom/bloom-3b", "name": "BLOOM-3B", "maker": "bigscience", "family": "bigscience/bloom", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2233, "dossier_id": "bigscience/bloom/bloom-560m", "name": "BLOOM-560M", "maker": "bigscience", "family": "bigscience/bloom", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2234, "dossier_id": "bigscience/bloom/bloom-7b1", "name": "BLOOM-7.1B", "maker": "bigscience", "family": "bigscience/bloom", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2235, "dossier_id": "bigscience/bloomz/bloomz", "name": "BLOOMZ", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2236, "dossier_id": "bigscience/bloomz/bloomz-1b1", "name": "BLOOMZ-1.1B", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2237, "dossier_id": "bigscience/bloomz/bloomz-1b7", "name": "BLOOMZ-1.7B", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2238, "dossier_id": "bigscience/bloomz/bloomz-3b", "name": "BLOOMZ-3B", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2239, "dossier_id": "bigscience/bloomz/bloomz-560m", "name": "BLOOMZ-560M", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2240, "dossier_id": "bigscience/bloomz/bloomz-7b1", "name": "BLOOMZ-7.1B", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2241, "dossier_id": "bigscience/bloomz/bloomz-7b1-mt", "name": "BLOOMZ-7.1B-MT", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2242, "dossier_id": "bigscience/bloomz/bloomz-7b1-p3", "name": "BLOOMZ-7.1B-P3", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2243, "dossier_id": "bigscience/bloomz/bloomz-mt", "name": "BLOOMZ-MT", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2244, "dossier_id": "bigscience/bloomz/bloomz-p3", "name": "BLOOMZ-P3", "maker": "bigscience", "family": "bigscience/bloomz", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2245, "dossier_id": "bigscience/santacoder/santacoder", "name": "SantaCoder", "maker": "bigscience", "family": "bigscience/santacoder", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2246, "dossier_id": "bigscience/santacoder/santacoderpack", "name": "SantaCoderPack", "maker": "bigscience", "family": "bigscience/santacoder", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2247, "dossier_id": "bigscience/sgpt/sgpt-bloom-7b1-msmarco", "name": "SGPT-BLOOM-7.1B-MSMARCO", "maker": "bigscience", "family": "bigscience/sgpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2248, "dossier_id": "bit/mindllm/mindllm-1-3b-chat-zh-v2-0", "name": "MindLLM-1.3B-Chat-zh-v2.0", "maker": "bit", "family": "bit/mindllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2249, "dossier_id": "bit/mindllm/mindllm-3b", "name": "MindLLM-3B", "maker": "bit", "family": "bit/mindllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2250, "dossier_id": "bjtu/transgpt/transgpt-7b", "name": "TransGPT-7B", "maker": "bjtu", "family": "bjtu/transgpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2251, "dossier_id": "bonn/tucano/tucano-160m", "name": "Tucano-160m", "maker": "bonn", "family": "bonn/tucano", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2252, "dossier_id": "bonn/tucano/tucano-1b1", "name": "Tucano-1b1", "maker": "bonn", "family": "bonn/tucano", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2253, "dossier_id": "bonn/tucano/tucano-1b1-instruct", "name": "Tucano-1b1-Instruct", "maker": "bonn", "family": "bonn/tucano", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2254, "dossier_id": "bonn/tucano/tucano-630m", "name": "Tucano-630m", "maker": "bonn", "family": "bonn/tucano", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2255, "dossier_id": "bonn/vitucano/vitucano-1b5-v1", "name": "ViTucano-1b5-v1", "maker": "bonn", "family": "bonn/vitucano", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2256, "dossier_id": "buaa/tinyllava/tinyllava-1-5b", "name": "TinyLLaVA-1.5B", "maker": "buaa", "family": "buaa/tinyllava", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2257, "dossier_id": "buaa/tinyllava/tinyllava-2-0b", "name": "TinyLLaVA-2.0B", "maker": "buaa", "family": "buaa/tinyllava", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2258, "dossier_id": "buaa/tinyllava/tinyllava-gemma-siglip-2-4b", "name": "TinyLLaVA-Gemma-SigLIP-2.4B", "maker": "buaa", "family": "buaa/tinyllava", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2259, "dossier_id": "buaa/tinyllava/tinyllava-openelm-siglip-0-89b", "name": "TinyLLaVA-OpenELM-450M-SigLIP-0.89B", "maker": "buaa", "family": "buaa/tinyllava", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2260, "dossier_id": "buaa/tinyllava/tinyllava-qwen2-0-5b-siglip", "name": "TinyLLaVA-Qwen2-0.5B-SigLIP", "maker": "buaa", "family": "buaa/tinyllava", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2261, "dossier_id": "buaa/tinyllava/tinyllava-qwen2-5-3b-siglip", "name": "TinyLLaVA-Qwen2.5-3B-SigLIP", "maker": "buaa", "family": "buaa/tinyllava", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2262, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2263, "dossier_id": "bupt/phonelm/phonelm-0-5b", "name": "PhoneLM-0.5B", "maker": "bupt", "family": "bupt/phonelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2264, "dossier_id": "bupt/phonelm/phonelm-0-5b-instruct", "name": "PhoneLM-0.5B-Instruct", "maker": "bupt", "family": "bupt/phonelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2265, "dossier_id": "bupt/phonelm/phonelm-1-5b", "name": "PhoneLM-1.5B", "maker": "bupt", "family": "bupt/phonelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2266, "dossier_id": "bupt/phonelm/phonelm-1-5b-call", "name": "PhoneLM-1.5B-Call", "maker": "bupt", "family": "bupt/phonelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2267, "dossier_id": "bupt/phonelm/phonelm-1-5b-instruct", "name": "PhoneLM-1.5B-Instruct", "maker": "bupt", "family": "bupt/phonelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2268, "dossier_id": "cerebras/cerebras-gpt/cerebras-gpt-1-3b", "name": "Cerebras-GPT 1.3B", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2269, "dossier_id": "cerebras/cerebras-gpt/cerebras-gpt-111m", "name": "Cerebras-GPT 111M", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2270, "dossier_id": "cerebras/cerebras-gpt/cerebras-gpt-13b", "name": "Cerebras-GPT 13B", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2271, "dossier_id": "cerebras/cerebras-gpt/cerebras-gpt-2-7b", "name": "Cerebras-GPT 2.7B", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2272, "dossier_id": "cerebras/cerebras-gpt/cerebras-gpt-256m", "name": "Cerebras-GPT 256M", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2273, "dossier_id": "cerebras/cerebras-gpt/cerebras-gpt-590m", "name": "Cerebras-GPT 590M", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2274, "dossier_id": "cerebras/cerebras-gpt/cerebras-gpt-6-7b", "name": "Cerebras-GPT 6.7B", "maker": "cerebras", "family": "cerebras/cerebras-gpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2275, "dossier_id": "cityu/compare2score/compare2score", "name": "Compare2Score", "maker": "cityu", "family": "cityu/compare2score", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2276, "dossier_id": "cmu/polycoder/polycoder-2-7b", "name": "PolyCoder 2.7B", "maker": "cmu", "family": "cmu/polycoder", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2277, "dossier_id": "cyberagent/open-calm/open-calm-1b", "name": "OpenCALM-1B", "maker": "cyberagent", "family": "cyberagent/open-calm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2278, "dossier_id": "databricks/dolly/dolly-v1-6b", "name": "Dolly v1 6B", "maker": "databricks", "family": "databricks/dolly", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2279, "dossier_id": "databricks/dolly/dolly-v2-12b", "name": "Dolly v2 12B", "maker": "databricks", "family": "databricks/dolly", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2280, "dossier_id": "databricks/dolly/dolly-v2-3b", "name": "Dolly v2 3B", "maker": "databricks", "family": "databricks/dolly", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2281, "dossier_id": "databricks/dolly/dolly-v2-7b", "name": "Dolly v2 7B", "maker": "databricks", "family": "databricks/dolly", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2282, "dossier_id": "databricks/mpt/mpt-7b", "name": "MPT-7B", "maker": "databricks", "family": "databricks/mpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2283, "dossier_id": "databricks/mpt/mpt-7b-chat", "name": "MPT-7B-Chat", "maker": "databricks", "family": "databricks/mpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-turn chat demo. MosaicML released it as a chatbot-style fine-tune and marked it research-only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2284, "dossier_id": "databricks/mpt/mpt-7b-instruct", "name": "MPT-7B-Instruct", "maker": "databricks", "family": "databricks/mpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2285, "dossier_id": "ecnu/educhat/educhat-base-002-13b", "name": "EduChat-Base-002-13B", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2286, "dossier_id": "ecnu/educhat/educhat-base-002-7b", "name": "EduChat-Base-002-7B", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2287, "dossier_id": "ecnu/educhat/educhat-sft-002-13b", "name": "EduChat-SFT-002-13B", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2288, "dossier_id": "ecnu/educhat/educhat-sft-002-7b", "name": "EduChat-SFT-002-7B", "maker": "ecnu", "family": "ecnu/educhat", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2289, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2290, "dossier_id": "eleutherai/deep-ignorance/deep-ignorance-e2e-strong-filter", "name": "Deep Ignorance end-to-end strong filter", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2291, "dossier_id": "eleutherai/deep-ignorance/deep-ignorance-e2e-weak-filter", "name": "Deep Ignorance end-to-end weak filter", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2292, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2293, "dossier_id": "eleutherai/deep-ignorance/deep-ignorance-unfiltered", "name": "Deep Ignorance unfiltered", "maker": "eleutherai", "family": "eleutherai/deep-ignorance", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2294, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2295, "dossier_id": "eleutherai/gpt-j/gpt-j-6b", "name": "GPT-J-6B", "maker": "eleutherai", "family": "eleutherai/gpt-j", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2296, "dossier_id": "eleutherai/gpt-neo/gpt-neo-1-3b", "name": "GPT-Neo 1.3B", "maker": "eleutherai", "family": "eleutherai/gpt-neo", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2297, "dossier_id": "eleutherai/gpt-neo/gpt-neo-125m", "name": "GPT-Neo 125M", "maker": "eleutherai", "family": "eleutherai/gpt-neo", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2298, "dossier_id": "eleutherai/gpt-neo/gpt-neo-2-7b", "name": "GPT-Neo 2.7B", "maker": "eleutherai", "family": "eleutherai/gpt-neo", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2299, "dossier_id": "eleutherai/gpt-neox/gpt-neox-20b", "name": "GPT-NeoX-20B", "maker": "eleutherai", "family": "eleutherai/gpt-neox", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2300, "dossier_id": "eleutherai/polyglot/polyglot-ko-1-3b", "name": "Polyglot-Ko 1.3B", "maker": "eleutherai", "family": "eleutherai/polyglot", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2301, "dossier_id": "eleutherai/polyglot/polyglot-ko-12-8b", "name": "Polyglot-Ko 12.8B", "maker": "eleutherai", "family": "eleutherai/polyglot", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2302, "dossier_id": "eleutherai/polyglot/polyglot-ko-3-8b", "name": "Polyglot-Ko 3.8B", "maker": "eleutherai", "family": "eleutherai/polyglot", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2303, "dossier_id": "eleutherai/polyglot/polyglot-ko-5-8b", "name": "Polyglot-Ko 5.8B", "maker": "eleutherai", "family": "eleutherai/polyglot", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2304, "dossier_id": "eleutherai/polypythia/polypythia-14m", "name": "PolyPythia 14M", "maker": "eleutherai", "family": "eleutherai/polypythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2305, "dossier_id": "eleutherai/polypythia/polypythia-160m", "name": "PolyPythia 160M", "maker": "eleutherai", "family": "eleutherai/polypythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2306, "dossier_id": "eleutherai/polypythia/polypythia-31m", "name": "PolyPythia 31M", "maker": "eleutherai", "family": "eleutherai/polypythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2307, "dossier_id": "eleutherai/polypythia/polypythia-410m", "name": "PolyPythia 410M", "maker": "eleutherai", "family": "eleutherai/polypythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2308, "dossier_id": "eleutherai/polypythia/polypythia-70m", "name": "PolyPythia 70M", "maker": "eleutherai", "family": "eleutherai/polypythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2309, "dossier_id": "eleutherai/pythia/pythia-1-4b", "name": "Pythia 1.4B", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2310, "dossier_id": "eleutherai/pythia/pythia-1-4b-deduped", "name": "Pythia 1.4B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2311, "dossier_id": "eleutherai/pythia/pythia-12b", "name": "Pythia 12B", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2312, "dossier_id": "eleutherai/pythia/pythia-12b-deduped", "name": "Pythia 12B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2313, "dossier_id": "eleutherai/pythia/pythia-14m", "name": "Pythia 14M", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2314, "dossier_id": "eleutherai/pythia/pythia-14m-deduped", "name": "Pythia 14M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2315, "dossier_id": "eleutherai/pythia/pythia-160m", "name": "Pythia 160M", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2316, "dossier_id": "eleutherai/pythia/pythia-160m-deduped", "name": "Pythia 160M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2317, "dossier_id": "eleutherai/pythia/pythia-1b", "name": "Pythia 1B", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2318, "dossier_id": "eleutherai/pythia/pythia-1b-deduped", "name": "Pythia 1B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2319, "dossier_id": "eleutherai/pythia/pythia-2-8b", "name": "Pythia 2.8B", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2320, "dossier_id": "eleutherai/pythia/pythia-2-8b-deduped", "name": "Pythia 2.8B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2321, "dossier_id": "eleutherai/pythia/pythia-31m", "name": "Pythia 31M", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2322, "dossier_id": "eleutherai/pythia/pythia-31m-deduped", "name": "Pythia 31M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2323, "dossier_id": "eleutherai/pythia/pythia-410m", "name": "Pythia 410M", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2324, "dossier_id": "eleutherai/pythia/pythia-410m-deduped", "name": "Pythia 410M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2325, "dossier_id": "eleutherai/pythia/pythia-6-9b", "name": "Pythia 6.9B", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2326, "dossier_id": "eleutherai/pythia/pythia-6-9b-deduped", "name": "Pythia 6.9B deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2327, "dossier_id": "eleutherai/pythia/pythia-70m", "name": "Pythia 70M", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2328, "dossier_id": "eleutherai/pythia/pythia-70m-deduped", "name": "Pythia 70M deduped", "maker": "eleutherai", "family": "eleutherai/pythia", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2329, "dossier_id": "epfl/meditron/meditron-7b", "name": "Meditron 7B", "maker": "epfl", "family": "epfl/meditron", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2330, "dossier_id": "evolutionaryscale/esm3/esm3-large", "name": "ESM3-large", "maker": "evolutionaryscale", "family": "evolutionaryscale/esm3", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2331, "dossier_id": "evolutionaryscale/esm3/esm3-medium", "name": "ESM3-medium", "maker": "evolutionaryscale", "family": "evolutionaryscale/esm3", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2332, "dossier_id": "evolutionaryscale/esm3/esm3-open", "name": "ESM3-open", "maker": "evolutionaryscale", "family": "evolutionaryscale/esm3", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2333, "dossier_id": "evolutionaryscale/esm3/esm3-small", "name": "ESM3-small", "maker": "evolutionaryscale", "family": "evolutionaryscale/esm3", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2334, "dossier_id": "evolutionaryscale/esmc/esmc-300m", "name": "ESMC 300M", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmc", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2335, "dossier_id": "evolutionaryscale/esmc/esmc-600m", "name": "ESMC 600M", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmc", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2336, "dossier_id": "evolutionaryscale/esmc/esmc-6b", "name": "ESMC 6B", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmc", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2337, "dossier_id": "fudan/moss/moss-moon-003-base", "name": "MOSS-003 base", "maker": "fudan", "family": "fudan/moss", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2338, "dossier_id": "fudan/moss/moss-moon-003-sft", "name": "MOSS-003 SFT", "maker": "fudan", "family": "fudan/moss", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says the fine-tune follows instructions, holds multi-turn dialogue, and refuses inappropriate requests.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2339, "dossier_id": "fudan/moss/moss-moon-003-sft-plugin", "name": "MOSS-003 plugin", "maker": "fudan", "family": "fudan/moss", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2340, "dossier_id": "glasgow/libra/libra-v1-0-7b", "name": "Libra v1.0 7B", "maker": "glasgow", "family": "glasgow/libra", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2341, "dossier_id": "google/embeddinggemma/embeddinggemma", "name": "EmbeddingGemma", "maker": "google", "family": "google/embeddinggemma", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings for retrieval, classification, and clustering on device.", "summary_intro": "Google open multilingual text embedding model based on Gemma 3. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2342, "dossier_id": "google/gemini-embedding/gemini-embedding-001", "name": "Gemini Embedding", "maker": "google", "family": "google/gemini-embedding", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google describes it for semantic search, classification, and RAG. Gemini Embedding 2 is the recommended multimodal replacement.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2343, "dossier_id": "hu-berlin/boldt/boldt-dc-1b", "name": "Boldt-DC-1B", "maker": "hu-berlin", "family": "hu-berlin/boldt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2344, "dossier_id": "hu-berlin/boldt/boldt-dc-350m", "name": "Boldt-DC-350M", "maker": "hu-berlin", "family": "hu-berlin/boldt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2345, "dossier_id": "huggingface/cosmo/cosmo-1b", "name": "Cosmo-1B", "maker": "huggingface", "family": "huggingface/cosmo", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A pre-SmolLM Hugging Face text model trained on Cosmopedia synthetic textbooks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2346, "dossier_id": "huggingface/idefics/idefics-9b", "name": "IDEFICS-9B", "maker": "huggingface", "family": "huggingface/idefics", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 9B base IDEFICS model for image-text tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2347, "dossier_id": "huggingface/idefics/idefics-9b-instruct", "name": "IDEFICS-9B-Instruct", "maker": "huggingface", "family": "huggingface/idefics", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned 9B IDEFICS.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2348, "dossier_id": "huggingface/nanowhale/nanowhale-100m", "name": "nanowhale-100m", "maker": "huggingface", "family": "huggingface/nanowhale", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face reference SFT chat checkpoint of nanowhale used to implement DeepSeek-V4 ideas at tiny scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2349, "dossier_id": "huggingface/nanowhale/nanowhale-100m-base", "name": "nanowhale-100m-base", "maker": "huggingface", "family": "huggingface/nanowhale", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face reference pretrained base of the nanowhale reference model used to implement DeepSeek-V4 ideas at tiny scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2350, "dossier_id": "huggingface/smollm/smollm-1-7b", "name": "SmolLM-1.7B", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 1.7B scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2351, "dossier_id": "huggingface/smollm/smollm-1-7b-instruct", "name": "SmolLM-1.7B Instruct", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 1.7B scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2352, "dossier_id": "huggingface/smollm/smollm-135m", "name": "SmolLM-135M", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 135M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2353, "dossier_id": "huggingface/smollm/smollm-135m-instruct", "name": "SmolLM-135M Instruct", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 135M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2354, "dossier_id": "huggingface/smollm/smollm-360m", "name": "SmolLM-360M", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) base checkpoint for fine-tuning at the 360M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2355, "dossier_id": "huggingface/smollm/smollm-360m-instruct", "name": "SmolLM-360M Instruct", "maker": "huggingface", "family": "huggingface/smollm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face Smol Models Research (HuggingFaceTB) instruction-tuned checkpoint for local chat at the 360M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2356, "dossier_id": "ibm/granite-code/granite-3b-code-base-2k", "name": "Granite 3B Code Base 2K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2357, "dossier_id": "ibm/granite-code/granite-3b-code-instruct-2k", "name": "Granite 3B Code Instruct 2K", "maker": "ibm", "family": "ibm/granite-code", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2358, "dossier_id": "ibm/granite-timeseries/granite-timeseries-flowstate-r1", "name": "Granite Timeseries Flowstate R1", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2359, "dossier_id": "ideogram/ideogram/ideogram-4-0", "name": "Ideogram 4.0", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2360, "dossier_id": "iiith/akshara/akshara-v1", "name": "Akshara v1", "maker": "iiith", "family": "iiith/akshara", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2361, "dossier_id": "iiith/akshara/nanochat-telugu-560m", "name": "NanoChat Telugu 560M", "maker": "iiith", "family": "iiith/akshara", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2362, "dossier_id": "iitb/param/agriparam", "name": "AgriParam", "maker": "iitb", "family": "iitb/param", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2363, "dossier_id": "iitb/param/ayurparam", "name": "AyurParam", "maker": "iitb", "family": "iitb/param", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2364, "dossier_id": "iitb/param/financeparam", "name": "FinanceParam", "maker": "iitb", "family": "iitb/param", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2365, "dossier_id": "iitb/param/legalparam", "name": "LegalParam", "maker": "iitb", "family": "iitb/param", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2366, "dossier_id": "iitb/param/param-1", "name": "Param-1", "maker": "iitb", "family": "iitb/param", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2367, "dossier_id": "iitb/param/param-1-2-9b-instruct", "name": "Param-1 2.9B Instruct", "maker": "iitb", "family": "iitb/param", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2368, "dossier_id": "iitgn/ganga/ganga-1b", "name": "Ganga-1B", "maker": "iitgn", "family": "iitgn/ganga", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2369, "dossier_id": "iitgn/ganga/ganga-2-1b", "name": "Ganga-2-1B", "maker": "iitgn", "family": "iitgn/ganga", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2370, "dossier_id": "innsbruck/smallreason/smallreason-colbert-32m", "name": "SmallReason-ColBERT-32M", "maker": "innsbruck", "family": "innsbruck/smallreason", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2371, "dossier_id": "innsbruck/uibk-embedding/uibk-embedding-v1", "name": "UIBK-Embedding-v1", "maker": "innsbruck", "family": "innsbruck/uibk-embedding", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2372, "dossier_id": "inria/manta/manta-lm-base", "name": "MANTa-LM base", "maker": "inria", "family": "inria/manta", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2373, "dossier_id": "kaist/selfee/selfee-13b", "name": "SelFee 13B", "maker": "kaist", "family": "kaist/selfee", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2374, "dossier_id": "kaist/selfee/selfee-7b", "name": "SelFee 7B", "maker": "kaist", "family": "kaist/selfee", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2375, "dossier_id": "kakao/kogpt/kogpt-6b-ryan1-5b", "name": "KoGPT 6B ryan1.5b", "maker": "kakao", "family": "kakao/kogpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The README says it is for Korean classification, search, summarization, and generation in research settings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2376, "dossier_id": "korea-university/kullm/kullm-polyglot-12-8b-v2", "name": "KULLM-Polyglot-12.8B-v2", "maker": "korea-university", "family": "korea-university/kullm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2377, "dossier_id": "korea-university/kullm/kullm-polyglot-12-8b-v3", "name": "KULLM-Polyglot-12.8B-v3", "maker": "korea-university", "family": "korea-university/kullm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not stated. The README is an empty file. The name places it in the KULLM Polyglot 12.8B line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2378, "dossier_id": "korea-university/kullm/kullm-polyglot-5-8b-v2", "name": "KULLM-Polyglot-5.8B-v2", "maker": "korea-university", "family": "korea-university/kullm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2379, "dossier_id": "kstate/hardware-phi/hardware-phi-1-5b", "name": "Hardware Phi-1.5B", "maker": "kstate", "family": "kstate/hardware-phi", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2380, "dossier_id": "lanzhou/tibetan/t-llama", "name": "T-LLaMA", "maker": "lanzhou", "family": "lanzhou/tibetan", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2381, "dossier_id": "lightblue/karasu/karasu-1-1b", "name": "Karasu 1.1B", "maker": "lightblue", "family": "lightblue/karasu", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2382, "dossier_id": "lightblue/mpt/japanese-mpt-7b", "name": "Japanese MPT-7B", "maker": "lightblue", "family": "lightblue/mpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Add Japanese web text, the Japanese subset of mC4, on top of the MPT-7B checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2383, "dossier_id": "lighton/pagnol/pagnol-xl", "name": "PAGnol-XL", "maker": "lighton", "family": "lighton/pagnol", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2384, "dossier_id": "line/japanese-large-lm/japanese-large-lm-1-7b", "name": "Japanese Large LM 1.7B", "maker": "line", "family": "line/japanese-large-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2385, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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:`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2386, "dossier_id": "line/japanese-large-lm/japanese-large-lm-3-6b", "name": "Japanese Large LM 3.6B", "maker": "line", "family": "line/japanese-large-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2387, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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:`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2388, "dossier_id": "line/sacpo/p-sacpo", "name": "P-SACPO", "maker": "line", "family": "line/sacpo", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2389, "dossier_id": "line/sacpo/sacpo", "name": "SACPO", "maker": "line", "family": "line/sacpo", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2390, "dossier_id": "ljubljana/gams/gams-1b", "name": "GaMS-1B", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2391, "dossier_id": "ljubljana/gams/gams-1b-chat", "name": "GaMS-1B-Chat", "maker": "ljubljana", "family": "ljubljana/gams", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat-style generation in Slovene. The base card says the 1B line is not a general-purpose assistant.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2392, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small v1.0 base released with the 13B model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2393, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2394, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2395, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full fine-tune on Dolly and OASST, without the JASTER mix used in the sibling checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2396, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full fine-tune on JASTER plus Dolly and OASST.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2397, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full fine-tune on JASTER, the project's converted Japanese NLP tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2398, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "LoRA version of the same Dolly, ichikara 003-001, and OASST mix.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2399, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "LoRA tune on Dolly and OASST.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2400, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "LoRA tune on JASTER, Dolly, and OASST.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2401, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "LoRA tune on JASTER.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2402, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First public LLM-jp 13B base, released 2023-10-20.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2403, "dossier_id": "mbzuai/jais/jais-family-13b", "name": "Jais Family 13B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2404, "dossier_id": "mbzuai/jais/jais-family-1p3b", "name": "Jais Family 1.3B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2405, "dossier_id": "mbzuai/jais/jais-family-2p7b", "name": "Jais Family 2.7B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2406, "dossier_id": "mbzuai/jais/jais-family-590m", "name": "Jais Family 590M", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2407, "dossier_id": "mbzuai/jais/jais-family-6p7b", "name": "Jais Family 6.7B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2408, "dossier_id": "mcgill/tlm/tlm-100m", "name": "TLM-100M", "maker": "mcgill", "family": "mcgill/tlm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2409, "dossier_id": "mcgill/tlm/tlm-120m", "name": "TLM-120M", "maker": "mcgill", "family": "mcgill/tlm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2410, "dossier_id": "mcgill/tlm/tlm-180m", "name": "TLM-180M", "maker": "mcgill", "family": "mcgill/tlm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2411, "dossier_id": "mcgill/tlm/tlm-20m", "name": "TLM-20M", "maker": "mcgill", "family": "mcgill/tlm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2412, "dossier_id": "mcgill/tlm/tlm-230m", "name": "TLM-230M", "maker": "mcgill", "family": "mcgill/tlm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2413, "dossier_id": "mcgill/tlm/tlm-40m", "name": "TLM-40M", "maker": "mcgill", "family": "mcgill/tlm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2414, "dossier_id": "mcgill/tlm/tlm-650m", "name": "TLM-650M", "maker": "mcgill", "family": "mcgill/tlm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2415, "dossier_id": "mcgill/tlm/tlm-70m", "name": "TLM-70M", "maker": "mcgill", "family": "mcgill/tlm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2416, "dossier_id": "microsoft/phi/phi-2", "name": "Phi-2", "maker": "microsoft", "family": "microsoft/phi", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2417, "dossier_id": "mila/amplify/amplify-120m", "name": "AMPLIFY 120M", "maker": "mila", "family": "mila/amplify", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2418, "dossier_id": "mila/amplify/amplify-350m", "name": "AMPLIFY 350M", "maker": "mila", "family": "mila/amplify", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2419, "dossier_id": "naver/hyperclova/hyperclova-39b", "name": "HyperCLOVA 39B", "maker": "naver", "family": "naver/hyperclova", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper uses it for in-context learning and prompt-tuning studies between the 13B and 82B scales.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2420, "dossier_id": "naver/hyperclova/hyperclova-82b", "name": "HyperCLOVA 82B", "maker": "naver", "family": "naver/hyperclova", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper presents it for Korean in-context learning and for HyperCLOVA Studio, a prompt interface aimed at non-experts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2421, "dossier_id": "naver/lk/lk-b", "name": "LK-B", "maker": "naver", "family": "naver/lk", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It was a Studio engine for the pre-HyperCLOVA X completions mode, with tuning and without streaming.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2422, "dossier_id": "naver/lk/lk-d2", "name": "LK-D2", "maker": "naver", "family": "naver/lk", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2423, "dossier_id": "nomic/nomic-bert/nomic-bert-2048", "name": "NomicBERT 2048", "maker": "nomic", "family": "nomic/nomic-bert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Longer-context English masked language modeling, and as the starting encoder for Nomic Embed Text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2424, "dossier_id": "nomic/nomic-bert/nomic-xlm-2048", "name": "Nomic XLM 2048", "maker": "nomic", "family": "nomic/nomic-bert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual masked language modeling at 2048 tokens, and as the encoder behind Nomic Embed Text v2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2425, "dossier_id": "northeastern/moxin/moxin-7b-vlm", "name": "Moxin 7B VLM", "maker": "northeastern", "family": "northeastern/moxin", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2426, "dossier_id": "novelai/novelai-lm/calliope", "name": "Calliope", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2427, "dossier_id": "novelai/novelai-lm/euterpe", "name": "Euterpe", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2428, "dossier_id": "novelai/novelai-lm/krake", "name": "Krake", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2429, "dossier_id": "novelai/novelai-lm/sigurd", "name": "Sigurd", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2430, "dossier_id": "ntnu/norgpt/norgpt-369m", "name": "NorGPT-369M", "maker": "ntnu", "family": "ntnu/norgpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2431, "dossier_id": "ntnu/norgpt/norgpt-3b", "name": "NorGPT-3B", "maker": "ntnu", "family": "ntnu/norgpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2432, "dossier_id": "ntnu/norgpt/norgpt-3b-continue", "name": "NorGPT-3B-continue", "maker": "ntnu", "family": "ntnu/norgpt", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2433, "dossier_id": "ntnu/norllama/norllama-3b", "name": "NorLlama-3B", "maker": "ntnu", "family": "ntnu/norllama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2434, "dossier_id": "oslo/dual-lm/dual-lm-470m", "name": "Dual LM 470M", "maker": "oslo", "family": "oslo/dual-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2435, "dossier_id": "oslo/norbloom/norbloom-7b-scratch", "name": "NorBLOOM-7B-scratch", "maker": "oslo", "family": "oslo/norbloom", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2436, "dossier_id": "oslo/normistral/normistral-7b-scratch", "name": "NorMistral-7B-scratch", "maker": "oslo", "family": "oslo/normistral", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2437, "dossier_id": "oslo/normistral/normistral-7b-warm", "name": "NorMistral-7B-warm", "maker": "oslo", "family": "oslo/normistral", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2438, "dossier_id": "paris-saclay/croissantllm/croissantcool", "name": "CroissantCool-v0.2", "maker": "paris-saclay", "family": "paris-saclay/croissantllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2439, "dossier_id": "paris-saclay/croissantllm/croissantllm-base", "name": "CroissantLLMBase", "maker": "paris-saclay", "family": "paris-saclay/croissantllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2440, "dossier_id": "paris-saclay/croissantllm/croissantllm-chat", "name": "CroissantLLMChat-v0.1", "maker": "paris-saclay", "family": "paris-saclay/croissantllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2441, "dossier_id": "preferred-networks/plamo-2/plamo-2-1b", "name": "PLaMo 2 1B", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2442, "dossier_id": "preferred-networks/plamo-2/plamo-2-8b", "name": "PLaMo 2 8B", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2443, "dossier_id": "riken/fugaku-llm/fugaku-llm-13b", "name": "Fugaku-LLM-13B", "maker": "riken", "family": "riken/fugaku-llm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2444, "dossier_id": "riken/fugaku-llm/fugaku-llm-13b-instruct", "name": "Fugaku-LLM-13B-instruct", "maker": "riken", "family": "riken/fugaku-llm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2445, "dossier_id": "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b", "name": "Bilingual GPT-NeoX 4B", "maker": "rinna", "family": "rinna/bilingual-gpt-neox", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-Japanese bilingual GPT-NeoX pretrained from scratch on about 524B tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2446, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT plus PPO alignment of Bilingual GPT-NeoX 4B, following the InstructGPT recipe.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2447, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2448, "dossier_id": "rinna/gpt-neox/japanese-gpt-neox-3-6b", "name": "Japanese GPT-NeoX 3.6B", "maker": "rinna", "family": "rinna/gpt-neox", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "3.6B Japanese GPT-NeoX pretrained from scratch. It is the base for rinna's 3.6B instruction models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2449, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "RLHF-aligned instruction model. SFT then PPO, following the InstructGPT setup, starting from the 3.6B SFT-v2 checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2450, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2451, "dossier_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", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2452, "dossier_id": "rinna/gpt-neox/japanese-gpt-neox-small", "name": "Japanese GPT-NeoX Small", "maker": "rinna", "family": "rinna/gpt-neox", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Japanese GPT-NeoX pretrained from scratch on Japanese CC-100, Japanese C4, and Japanese Wikipedia.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2453, "dossier_id": "rinna/vicuna/vicuna-13b-delta-langchain-mrkl", "name": "Vicuna 13B Delta LangChain MRKL", "maker": "rinna", "family": "rinna/vicuna", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A small experimental finetune of Vicuna 13B on 15 LangChain MRKL-format examples, released by rinna as a delta.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2454, "dossier_id": "ruc/yulan/yulan-chat-1-65b-v2", "name": "YuLan-Chat-1-65B-v2", "maker": "ruc", "family": "ruc/yulan", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2455, "dossier_id": "saarland/smole/smole-bart", "name": "Smole-BART", "maker": "saarland", "family": "saarland/smole", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2456, "dossier_id": "sambanova/bloomchat/bloomchat-176b-v1", "name": "BLOOMChat 176B v1", "maker": "sambanova", "family": "sambanova/bloomchat", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2457, "dossier_id": "scu/luban/luban-10b", "name": "Luban-10B", "maker": "scu", "family": "scu/luban", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2458, "dossier_id": "scut/bianque/bianque-2", "name": "BianQue-2.0", "maker": "scut", "family": "scut/bianque", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2459, "dossier_id": "sdu/fuzi/fuzi-mingcha", "name": "\u592b\u5b50\u00b7\u660e\u5bdf", "maker": "sdu", "family": "sdu/fuzi", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2460, "dossier_id": "sesame/csm/csm-1b", "name": "CSM-1B", "maker": "sesame", "family": "sesame/csm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2461, "dossier_id": "sesame/csm/csm-medium", "name": "CSM Medium", "maker": "sesame", "family": "sesame/csm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2462, "dossier_id": "sesame/csm/csm-small", "name": "CSM Small", "maker": "sesame", "family": "sesame/csm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2463, "dossier_id": "sjtu/abel/abel-70b-001", "name": "Abel-70B-001", "maker": "sjtu", "family": "sjtu/abel", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named Abel math-series checkpoint. The repo does not say which tasks it was tuned for.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2464, "dossier_id": "softbank/sarashina1/sarashina1-13b", "name": "Sarashina1-13B", "maker": "softbank", "family": "softbank/sarashina1", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2465, "dossier_id": "softbank/sarashina1/sarashina1-65b", "name": "Sarashina1-65B", "maker": "softbank", "family": "softbank/sarashina1", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2466, "dossier_id": "softbank/sarashina1/sarashina1-7b", "name": "Sarashina1-7B", "maker": "softbank", "family": "softbank/sarashina1", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2467, "dossier_id": "softbank/tiny-lm/tiny-lm", "name": "tiny-lm", "maker": "softbank", "family": "softbank/tiny-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Library and pipeline tests. The vendor does not present it as a Sarashina product model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2468, "dossier_id": "softbank/tiny-lm/tiny-lm-chat", "name": "tiny-lm-chat", "maker": "softbank", "family": "softbank/tiny-lm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Library and pipeline tests. The vendor does not present it as a Sarashina product model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2469, "dossier_id": "stockmark/stockmark-llm/stockmark-13b", "name": "Stockmark-13b", "maker": "stockmark", "family": "stockmark/stockmark-llm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2470, "dossier_id": "szu/chinese-falcon/chinese-falcon-7b", "name": "Chinese-Falcon-7B", "maker": "szu", "family": "szu/chinese-falcon", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2471, "dossier_id": "szu/chinese-llama/chinese-llama-13b", "name": "Chinese-LLaMA-13B", "maker": "szu", "family": "szu/chinese-llama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2472, "dossier_id": "szu/chinese-llama/chinese-llama-33b", "name": "Chinese-LLaMA-33B", "maker": "szu", "family": "szu/chinese-llama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2473, "dossier_id": "szu/chinese-llama/chinese-llama-7b", "name": "Chinese-LLaMA-7B", "maker": "szu", "family": "szu/chinese-llama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2474, "dossier_id": "szu/chinese-llama-2/chinese-llama-2-13b", "name": "Chinese-LLaMA-2-13B", "maker": "szu", "family": "szu/chinese-llama-2", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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 `", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2475, "dossier_id": "szu/chinese-llama-2/chinese-llama-2-7b", "name": "Chinese-LLaMA-2-7B", "maker": "szu", "family": "szu/chinese-llama-2", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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 `", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2476, "dossier_id": "szu/openllama/openllama-13b", "name": "Linly-OpenLLaMA-13B", "maker": "szu", "family": "szu/openllama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2477, "dossier_id": "tau/tavbert/tavbert-ar", "name": "TavBERT Arabic", "maker": "tau", "family": "tau/tavbert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2478, "dossier_id": "tau/tavbert/tavbert-he", "name": "TavBERT Hebrew", "maker": "tau", "family": "tau/tavbert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2479, "dossier_id": "tau/tavbert/tavbert-tr", "name": "TavBERT Turkish", "maker": "tau", "family": "tau/tavbert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2480, "dossier_id": "tii/falcon/falcon-40b", "name": "Falcon-40B", "maker": "tii", "family": "tii/falcon", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2481, "dossier_id": "tii/falcon/falcon-7b", "name": "Falcon-7B", "maker": "tii", "family": "tii/falcon", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2482, "dossier_id": "tii/falcon/falcon-rw-1b", "name": "Falcon-RW-1B", "maker": "tii", "family": "tii/falcon", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2483, "dossier_id": "tii/falcon/falcon-rw-7b", "name": "Falcon-RW-7B", "maker": "tii", "family": "tii/falcon", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2484, "dossier_id": "together/m2-bert/m2-bert-80m-2k", "name": "M2-BERT-80M-2k", "maker": "together", "family": "together/m2-bert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released M2-BERT-80M-2k (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2485, "dossier_id": "together/m2-bert/m2-bert-80m-2k-retrieval", "name": "M2-BERT-80M-2k-retrieval", "maker": "together", "family": "together/m2-bert", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released M2-BERT-80M-2k-retrieval (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2486, "dossier_id": "together/redpajama/redpajama-incite-7b-base", "name": "RedPajama-INCITE-7B-Base", "maker": "together", "family": "together/redpajama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released RedPajama-INCITE-7B-Base (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2487, "dossier_id": "together/redpajama/redpajama-incite-7b-chat", "name": "RedPajama-INCITE-7B-Chat", "maker": "together", "family": "together/redpajama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model for open or commercial dialogue. Together trained it only on open instruction data and exposed it through OpenChatKit.", "summary_intro": "Together AI released RedPajama-INCITE-7B-Chat (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2488, "dossier_id": "together/redpajama/redpajama-incite-7b-instruct", "name": "RedPajama-INCITE-7B-Instruct", "maker": "together", "family": "together/redpajama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released RedPajama-INCITE-7B-Instruct (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2489, "dossier_id": "together/redpajama/redpajama-incite-base-3b-v1", "name": "RedPajama-INCITE-Base-3B-v1", "maker": "together", "family": "together/redpajama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released RedPajama-INCITE-Base-3B-v1 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2490, "dossier_id": "together/redpajama/redpajama-incite-chat-3b-v1", "name": "RedPajama-INCITE-Chat-3B-v1", "maker": "together", "family": "together/redpajama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released RedPajama-INCITE-Chat-3B-v1 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2491, "dossier_id": "together/redpajama/redpajama-incite-instruct-3b-v1", "name": "RedPajama-INCITE-Instruct-3B-v1", "maker": "together", "family": "together/redpajama", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released RedPajama-INCITE-Instruct-3B-v1 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2492, "dossier_id": "tohoku/nanochat/nanochat-jp-v3-d22", "name": "nanochat-jp v3 d22", "maker": "tohoku", "family": "tohoku/nanochat", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2493, "dossier_id": "tohoku/nanochat/nanochat-jp-v4-d22", "name": "nanochat-jp v4 d22", "maker": "tohoku", "family": "tohoku/nanochat", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2494, "dossier_id": "u-tokyo/weblab/weblab-10b", "name": "Weblab-10B", "maker": "u-tokyo", "family": "u-tokyo/weblab", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2495, "dossier_id": "ubc/jasmine/jasmine-350m", "name": "Jasmine-350M", "maker": "ubc", "family": "ubc/jasmine", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2496, "dossier_id": "ucl/britllm/caernarfon-3b", "name": "Caernarfon 3B", "maker": "ucl", "family": "ucl/britllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2497, "dossier_id": "ucl/cuatrollm/cuatrollm", "name": "CuatroLLM", "maker": "ucl", "family": "ucl/cuatrollm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2498, "dossier_id": "ucl/monoweb/monoweb", "name": "MonoWeb", "maker": "ucl", "family": "ucl/monoweb", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2499, "dossier_id": "ucl/transwebllm/transwebllm", "name": "TransWebLLM", "maker": "ucl", "family": "ucl/transwebllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2500, "dossier_id": "ucl/transwebllm/transwebllm-cool", "name": "TransWebLLM-cool", "maker": "ucl", "family": "ucl/transwebllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2501, "dossier_id": "ucl/transwebllm/transwebllm-web", "name": "TransWebLLM-web", "maker": "ucl", "family": "ucl/transwebllm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2502, "dossier_id": "uct/mzansilm/mzansilm-125m", "name": "MzansiLM 125M", "maker": "uct", "family": "uct/mzansilm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2503, "dossier_id": "ufl/gatortron/gatortron-base-2k", "name": "GatorTron-Base-2k", "maker": "ufl", "family": "ufl/gatortron", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2504, "dossier_id": "umd/gemstone/gemstone-1024x28", "name": "Gemstone-1024x28", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2505, "dossier_id": "umd/gemstone/gemstone-1280x15", "name": "Gemstone-1280x15", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2506, "dossier_id": "umd/gemstone/gemstone-1280x36", "name": "Gemstone-1280x36", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2507, "dossier_id": "umd/gemstone/gemstone-1536x50", "name": "Gemstone-1536x50", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2508, "dossier_id": "umd/gemstone/gemstone-1792x18", "name": "Gemstone-1792x18", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2509, "dossier_id": "umd/gemstone/gemstone-1792x7", "name": "Gemstone-1792x7", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2510, "dossier_id": "umd/gemstone/gemstone-2048x27", "name": "Gemstone-2048x27", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2511, "dossier_id": "umd/gemstone/gemstone-2560x8", "name": "Gemstone-2560x8", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2512, "dossier_id": "umd/gemstone/gemstone-256x23", "name": "Gemstone-256x23", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2513, "dossier_id": "umd/gemstone/gemstone-256x27", "name": "Gemstone-256x27", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2514, "dossier_id": "umd/gemstone/gemstone-256x71", "name": "Gemstone-256x71", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2515, "dossier_id": "umd/gemstone/gemstone-256x80", "name": "Gemstone-256x80", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2516, "dossier_id": "umd/gemstone/gemstone-3072x12", "name": "Gemstone-3072x12", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2517, "dossier_id": "umd/gemstone/gemstone-384x13", "name": "Gemstone-384x13", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2518, "dossier_id": "umd/gemstone/gemstone-384x36", "name": "Gemstone-384x36", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2519, "dossier_id": "umd/gemstone/gemstone-512x11", "name": "Gemstone-512x11", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2520, "dossier_id": "umd/gemstone/gemstone-512x12", "name": "Gemstone-512x12", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2521, "dossier_id": "umd/gemstone/gemstone-512x13", "name": "Gemstone-512x13", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2522, "dossier_id": "umd/gemstone/gemstone-512x14", "name": "Gemstone-512x14", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2523, "dossier_id": "umd/gemstone/gemstone-512x16", "name": "Gemstone-512x16", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2524, "dossier_id": "umd/gemstone/gemstone-768x3", "name": "Gemstone-768x3", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2525, "dossier_id": "umd/gemstone/gemstone-768x45", "name": "Gemstone-768x45", "maker": "umd", "family": "umd/gemstone", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2526, "dossier_id": "unsw/darwin/darwin-7b", "name": "Darwin-7B", "maker": "unsw", "family": "unsw/darwin", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2527, "dossier_id": "usc/hubble/hubble-1b-100b-inject-0-25", "name": "Hubble 1B Timing 0-25% (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2528, "dossier_id": "usc/hubble/hubble-1b-100b-inject-0-50", "name": "Hubble 1B Timing 0-50% (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2529, "dossier_id": "usc/hubble/hubble-1b-100b-inject-25-50", "name": "Hubble 1B Timing 25-50% (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2530, "dossier_id": "usc/hubble/hubble-1b-100b-inject-50-100", "name": "Hubble 1B Timing 50-100% (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2531, "dossier_id": "usc/hubble/hubble-1b-100b-inject-50-75", "name": "Hubble 1B Timing 50-75% (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2532, "dossier_id": "usc/hubble/hubble-1b-100b-inject-75-100", "name": "Hubble 1B Timing 75-100% (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2533, "dossier_id": "usc/hubble/hubble-1b-100b-interference-copyright", "name": "Hubble 1B Interference Copyright (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2534, "dossier_id": "usc/hubble/hubble-1b-100b-interference-privacy", "name": "Hubble 1B Interference Privacy (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2535, "dossier_id": "usc/hubble/hubble-1b-100b-interference-testset", "name": "Hubble 1B Interference Test Set (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2536, "dossier_id": "usc/hubble/hubble-1b-100b-paraphrased", "name": "Hubble 1B Paraphrased (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2537, "dossier_id": "usc/hubble/hubble-1b-100b-perturbed", "name": "Hubble 1B Perturbed (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2538, "dossier_id": "usc/hubble/hubble-1b-100b-standard", "name": "Hubble 1B Standard (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2539, "dossier_id": "usc/hubble/hubble-1b-500b-perturbed", "name": "Hubble 1B Perturbed (500B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2540, "dossier_id": "usc/hubble/hubble-1b-500b-standard", "name": "Hubble 1B Standard (500B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2541, "dossier_id": "usc/hubble/hubble-8b-100b-paraphrased", "name": "Hubble 8B Paraphrased (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2542, "dossier_id": "usc/hubble/hubble-8b-100b-perturbed", "name": "Hubble 8B Perturbed (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2543, "dossier_id": "usc/hubble/hubble-8b-100b-standard", "name": "Hubble 8B Standard (100B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2544, "dossier_id": "usc/hubble/hubble-8b-500b-perturbed", "name": "Hubble 8B Perturbed (500B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2545, "dossier_id": "usc/hubble/hubble-8b-500b-standard", "name": "Hubble 8B Standard (500B tokens)", "maker": "usc", "family": "usc/hubble", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2546, "dossier_id": "vivo/bluelm/bluelm-7b-base", "name": "BlueLM-7B-Base", "maker": "vivo", "family": "vivo/bluelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2547, "dossier_id": "vivo/bluelm/bluelm-7b-chat", "name": "BlueLM-7B-Chat", "maker": "vivo", "family": "vivo/bluelm", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2548, "dossier_id": "waterloo/gaggle/gaggle-nanochat", "name": "Gaggle nanochat", "maker": "waterloo", "family": "waterloo/gaggle", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2549, "dossier_id": "writer/palmyra/palmyra-3b", "name": "Palmyra 3B", "maker": "writer", "family": "writer/palmyra", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2550, "dossier_id": "writer/palmyra/palmyra-base", "name": "Palmyra Base", "maker": "writer", "family": "writer/palmyra", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Writer's 5B English base decoder. The model card says Writer no longer maintains or supports it.", "summary_intro": "Writer's 5B English base decoder. The model card says Writer no longer maintains or supports it. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2551, "dossier_id": "writer/palmyra/palmyra-large", "name": "Palmyra Large", "maker": "writer", "family": "writer/palmyra", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2552, "dossier_id": "writer/palmyra/palmyra-small", "name": "Palmyra Small", "maker": "writer", "family": "writer/palmyra", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Writer's 128M English decoder, released as open weights for fast parsing, simple classification, address correction, and keyword recognition.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2553, "dossier_id": "wuerzburg/llammlein/llammlein-120m", "name": "LL\u00e4Mmlein 120M", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2554, "dossier_id": "wuerzburg/llammlein/llammlein-1b", "name": "LL\u00e4Mmlein 1B", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "context_length": 2048, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2555, "dossier_id": "openai/dalle/dall-e", "name": "DALL-E", "maker": "openai", "family": "openai/dalle", "context_length": 1280, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "12B text-to-image transformer announced January 5, 2021.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2556, "dossier_id": "google/gemini-robotics/gemini-robotics-on-device", "name": "Gemini Robotics On-Device", "maker": "google", "family": "google/gemini-robotics", "context_length": 1088, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2557, "dossier_id": "udel/dsm/dsm-150", "name": "DSM-150", "maker": "udel", "family": "udel/dsm", "context_length": 1026, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2558, "dossier_id": "udel/dsm/dsm-650", "name": "DSM-650", "maker": "udel", "family": "udel/dsm", "context_length": 1026, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2559, "dossier_id": "westlake/saprot/saprot-35m", "name": "SaProt 35M", "maker": "westlake", "family": "westlake/saprot", "context_length": 1026, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2560, "dossier_id": "westlake/saprot/saprot-650m", "name": "SaProt 650M", "maker": "westlake", "family": "westlake/saprot", "context_length": 1026, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2561, "dossier_id": "westlake/saprot/saprot-650m-inverse-folding", "name": "SaProt 650M inverse folding", "maker": "westlake", "family": "westlake/saprot", "context_length": 1026, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2562, "dossier_id": "inria/camembertv2/camembertv2-base", "name": "CamemBERTv2 base", "maker": "inria", "family": "inria/camembertv2", "context_length": 1025, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2563, "dossier_id": "abeja/gpt2/gpt2-large-japanese", "name": "gpt2-large-japanese", "maker": "abeja", "family": "abeja/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2564, "dossier_id": "alibaba/qwen-embed/tongyi-embedding-vision-flash", "name": "Tongyi-Embedding-Vision-Flash", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cheaper multimodal embedding. Singapore prices: image or video $0.03 and text $0.09 per million input tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2565, "dossier_id": "alibaba/qwen-embed/tongyi-embedding-vision-plus", "name": "Tongyi-Embedding-Vision-Plus", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multimodal embedding for cross-modal search. Singapore input price $0.09 per million tokens. Max tokens listed as 1,024.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2566, "dossier_id": "bar-ilan/qasrl/contextualizer-qasrl", "name": "QA-SRL contextualizer", "maker": "bar-ilan", "family": "bar-ilan/qasrl", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repo name says a contextualizer for QA-SRL. Usage is not documented on the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2567, "dossier_id": "basque/gpt2-eus/gpt2-eus-euscrawl", "name": "GPT-2 Eus EusCrawl", "maker": "basque", "family": "basque/gpt2-eus", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2568, "dossier_id": "basque/medical-mt5/medical-mt5-large", "name": "Medical mT5 large", "maker": "basque", "family": "basque/medical-mt5", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2569, "dossier_id": "bigscience/starencoder/starencoder", "name": "StarEncoder", "maker": "bigscience", "family": "bigscience/starencoder", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2570, "dossier_id": "bit/mindllm/mindllm-1-3b", "name": "MindLLM-1.3B", "maker": "bit", "family": "bit/mindllm", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2571, "dossier_id": "cornell/lmlm/lmlm-llama2-176m", "name": "LMLM-LLaMA2-176M", "maker": "cornell", "family": "cornell/lmlm", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2572, "dossier_id": "cornell/lmlm/lmlm-llama2-382m", "name": "LMLM-LLaMA2-382M", "maker": "cornell", "family": "cornell/lmlm", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2573, "dossier_id": "fudan/chinese-bart/bart-base-chinese", "name": "Chinese BART-base", "maker": "fudan", "family": "fudan/chinese-bart", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2574, "dossier_id": "fudan/chinese-bart/bart-large-chinese", "name": "Chinese BART-large", "maker": "fudan", "family": "fudan/chinese-bart", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2575, "dossier_id": "fudan/cpt/cpt-base", "name": "CPT-base", "maker": "fudan", "family": "fudan/cpt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2576, "dossier_id": "fudan/cpt/cpt-large", "name": "CPT-large", "maker": "fudan", "family": "fudan/cpt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2577, "dossier_id": "geneva/tim-unige/tim-unige-wmt24", "name": "TIM-UNIGE WMT24", "maker": "geneva", "family": "geneva/tim-unige", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2578, "dossier_id": "groningen/gpt2/gpt2-medium-dutch-embeddings", "name": "GPT-2 medium Dutch embeddings", "maker": "groningen", "family": "groningen/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2579, "dossier_id": "groningen/gpt2/gpt2-medium-italian-embeddings", "name": "GPT-2 medium Italian embeddings", "maker": "groningen", "family": "groningen/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2580, "dossier_id": "groningen/gpt2/gpt2-small-dutch", "name": "GPT-2 small Dutch", "maker": "groningen", "family": "groningen/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2581, "dossier_id": "groningen/gpt2/gpt2-small-dutch-embeddings", "name": "GPT-2 small Dutch embeddings", "maker": "groningen", "family": "groningen/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2582, "dossier_id": "groningen/gpt2/gpt2-small-italian", "name": "GPT-2 small Italian", "maker": "groningen", "family": "groningen/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2583, "dossier_id": "groningen/gpt2/gpt2-small-italian-embeddings", "name": "GPT-2 small Italian embeddings", "maker": "groningen", "family": "groningen/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2584, "dossier_id": "huggingface/distilbert/distilgpt2", "name": "DistilGPT-2", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face's distilled GPT-2 for cheap text generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2585, "dossier_id": "innsbruck/obliq-ir/obliq-ir-3b", "name": "OBLIQ-IR-3B", "maker": "innsbruck", "family": "innsbruck/obliq-ir", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2586, "dossier_id": "inria/camembertav2/camembertav2-base", "name": "CamemBERTav2 base", "maker": "inria", "family": "inria/camembertav2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2587, "dossier_id": "inria/manta/manta-lm-small", "name": "MANTa-LM small", "maker": "inria", "family": "inria/manta", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2588, "dossier_id": "jena/acemol/acemol", "name": "ACE-Mol", "maker": "jena", "family": "jena/acemol", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2589, "dossier_id": "jena/acemol/acemol-light", "name": "ACE-Mol-light", "maker": "jena", "family": "jena/acemol", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2590, "dossier_id": "jhu/kreyol-mt/kreyol-mt", "name": "Kreyol-MT", "maker": "jhu", "family": "jhu/kreyol-mt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2591, "dossier_id": "jhu/kreyol-mt/kreyol-mt-pubtrain", "name": "Kreyol-MT public data", "maker": "jhu", "family": "jhu/kreyol-mt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2592, "dossier_id": "jina/reranker/jina-reranker-v2-base-multilingual", "name": "jina-reranker-v2-base-multilingual", "maker": "jina", "family": "jina/reranker", "context_length": 1024, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller multilingual cross-encoder. Still served for users who do not want a Qwen-derived reranker.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2593, "dossier_id": "kyoto/bart/bart-base-japanese", "name": "Japanese BART base", "maker": "kyoto", "family": "kyoto/bart", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2594, "dossier_id": "kyoto/bart/bart-large-japanese", "name": "Japanese BART large", "maker": "kyoto", "family": "kyoto/bart", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2595, "dossier_id": "kyoto/gpt2/gpt2-large-japanese-char", "name": "Japanese GPT-2 large char", "maker": "kyoto", "family": "kyoto/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2596, "dossier_id": "kyoto/gpt2/gpt2-medium-japanese-char", "name": "Japanese GPT-2 medium char", "maker": "kyoto", "family": "kyoto/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2597, "dossier_id": "kyoto/gpt2/gpt2-small-japanese-char", "name": "Japanese GPT-2 small char", "maker": "kyoto", "family": "kyoto/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2598, "dossier_id": "lighton/pagnol/pagnol-large", "name": "PAGnol-large", "maker": "lighton", "family": "lighton/pagnol", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2599, "dossier_id": "lighton/pagnol/pagnol-medium", "name": "PAGnol-medium", "maker": "lighton", "family": "lighton/pagnol", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2600, "dossier_id": "lighton/pagnol/pagnol-small", "name": "PAGnol-small", "maker": "lighton", "family": "lighton/pagnol", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2601, "dossier_id": "linz/bio-xlstm/dna-xlstm-2m", "name": "DNA-xLSTM-2M", "maker": "linz", "family": "linz/bio-xlstm", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2602, "dossier_id": "linz/bio-xlstm/dna-xlstm-500k", "name": "DNA-xLSTM-500k", "maker": "linz", "family": "linz/bio-xlstm", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2603, "dossier_id": "ljubljana/gpt/gpt-sl-base", "name": "GPT-sl-base", "maker": "ljubljana", "family": "ljubljana/gpt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene next-token generation and a base for fine-tuning. The card does not present it as an instruction model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2604, "dossier_id": "lyon/zlata/zlata", "name": "Zlata", "maker": "lyon", "family": "lyon/zlata", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2605, "dossier_id": "lyon/zlata/zlata-tinystories", "name": "Zlata-TinyStories", "maker": "lyon", "family": "lyon/zlata", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2606, "dossier_id": "mcgill/dmas-llama-lite/dmas-llama-lite", "name": "DMaS-LLaMa-Lite", "maker": "mcgill", "family": "mcgill/dmas-llama-lite", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2607, "dossier_id": "mcgill/dmas-llama-lite/dmas-llama-lite-instruct", "name": "DMaS-LLaMa-Lite Instruct", "maker": "mcgill", "family": "mcgill/dmas-llama-lite", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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|>`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2608, "dossier_id": "nd/jam/jam-contextsum", "name": "Jam-Contextsum", "maker": "nd", "family": "nd/jam", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2609, "dossier_id": "northwestern/genomeocean/genomeocean-100m", "name": "GenomeOcean-100M", "maker": "northwestern", "family": "northwestern/genomeocean", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2610, "dossier_id": "northwestern/genomeocean/genomeocean-500m", "name": "GenomeOcean-500M", "maker": "northwestern", "family": "northwestern/genomeocean", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2611, "dossier_id": "patras/greekt5/greekt5-mt5-small-greeksum", "name": "GreekT5 (mt5-small-greeksum)", "maker": "patras", "family": "patras/greekt5", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2612, "dossier_id": "patras/greekt5/greekt5-umt5-base-greeksum", "name": "GreekT5 (umt5-base-greeksum)", "maker": "patras", "family": "patras/greekt5", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2613, "dossier_id": "patras/greekt5/greekt5-umt5-small-greeksum", "name": "GreekT5 (umt5-small-greeksum)", "maker": "patras", "family": "patras/greekt5", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2614, "dossier_id": "patras/greekwiki/greekwiki-umt5-base", "name": "GreekWiki (umt5-base)", "maker": "patras", "family": "patras/greekwiki", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2615, "dossier_id": "pisa/concretegpt/concretegpt", "name": "ConcreteGPT", "maker": "pisa", "family": "pisa/concretegpt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2616, "dossier_id": "polytechnique/ssml/ssml-text2breaks-fr-lora", "name": "SSML Text-to-Breaks French LoRA", "maker": "polytechnique", "family": "polytechnique/ssml", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2617, "dossier_id": "preferred-networks/generrna/generrna", "name": "GenerRNA", "maker": "preferred-networks", "family": "preferred-networks/generrna", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research de novo RNA generation and fine-tuning for properties such as protein binding. Not a chat LLM.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2618, "dossier_id": "rinna/gpt/japanese-gpt-1b", "name": "Japanese GPT 1B", "maker": "rinna", "family": "rinna/gpt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.3B-parameter Japanese GPT trained from scratch by rinna on Japanese C4, Japanese CC-100, and Japanese Wikipedia.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2619, "dossier_id": "rinna/gpt2/japanese-gpt2-medium", "name": "Japanese GPT-2 Medium", "maker": "rinna", "family": "rinna/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medium Japanese GPT-2, rinna's earliest public GPT-2 checkpoint, trained on Japanese CC-100 and Japanese Wikipedia.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2620, "dossier_id": "rinna/gpt2/japanese-gpt2-small", "name": "Japanese GPT-2 Small", "maker": "rinna", "family": "rinna/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Japanese GPT-2 for causal language modeling, trained by rinna on Japanese CC-100 and Japanese Wikipedia.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2621, "dossier_id": "rinna/gpt2/japanese-gpt2-xsmall", "name": "Japanese GPT-2 XSmall", "maker": "rinna", "family": "rinna/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Extra-small Japanese GPT-2 for causal language modeling, trained by rinna on Japanese CC-100 and Japanese Wikipedia.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2622, "dossier_id": "ruc/elmer/elmer", "name": "ELMER", "maker": "ruc", "family": "ruc/elmer", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2623, "dossier_id": "ruc/erya/erya", "name": "Erya", "maker": "ruc", "family": "ruc/erya", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2624, "dossier_id": "ruc/live/live-bart-base", "name": "LIVE-BART", "maker": "ruc", "family": "ruc/live", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2625, "dossier_id": "ruc/mvp/mvp", "name": "MVP", "maker": "ruc", "family": "ruc/mvp", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2626, "dossier_id": "salesforce/progen/progen2", "name": "ProGen2", "maker": "salesforce", "family": "salesforce/progen", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Protein sequence generation and fitness prediction without extra finetuning. Data variants cover general proteins and immune repertoires.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2627, "dossier_id": "smu/cottention/cottention-gpt-300m", "name": "Cottention GPT 300M", "maker": "smu", "family": "smu/cottention", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2628, "dossier_id": "stanford/anticipatory-music/anticipatory-music-large", "name": "Anticipatory Music Transformer Large", "maker": "stanford", "family": "stanford/anticipatory-music", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2629, "dossier_id": "stanford/anticipatory-music/anticipatory-music-medium", "name": "Anticipatory Music Transformer Medium", "maker": "stanford", "family": "stanford/anticipatory-music", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2630, "dossier_id": "stanford/anticipatory-music/anticipatory-music-small", "name": "Anticipatory Music Transformer Small", "maker": "stanford", "family": "stanford/anticipatory-music", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2631, "dossier_id": "stanford/anticipatory-music/autoregressive-music-small", "name": "Autoregressive Music Transformer Small", "maker": "stanford", "family": "stanford/anticipatory-music", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The release post lists it as the autoregressive baseline against the anticipatory models. It generates symbolic music left to right.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2632, "dossier_id": "stanford/biomedlm/biomedlm", "name": "BioMedLM", "maker": "stanford", "family": "stanford/biomedlm", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2633, "dossier_id": "stanford/crfm-gpt2/crfm-gpt2-medium", "name": "CRFM GPT-2 Medium", "maker": "stanford", "family": "stanford/crfm-gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2634, "dossier_id": "stanford/crfm-gpt2/crfm-gpt2-small", "name": "CRFM GPT-2 Small", "maker": "stanford", "family": "stanford/crfm-gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2635, "dossier_id": "stockmark/gpt-neox/gpt-neox-japanese-1-4b", "name": "GPT-NeoX Japanese 1.4B", "maker": "stockmark", "family": "stockmark/gpt-neox", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2636, "dossier_id": "szu/chatflow/chatflow-13b", "name": "Linly-ChatFlow-13B", "maker": "szu", "family": "szu/chatflow", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2637, "dossier_id": "szu/chatflow/chatflow-7b", "name": "Linly-ChatFlow-7B", "maker": "szu", "family": "szu/chatflow", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2638, "dossier_id": "tartu/gpt-est/gpt-for-est-base", "name": "GPT-for-EST base", "maker": "tartu", "family": "tartu/gpt-est", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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<.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2639, "dossier_id": "tartu/gpt-est/gpt-for-est-large", "name": "GPT-for-EST large", "maker": "tartu", "family": "tartu/gpt-est", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Estonian text generation with the same domain prefixes as the base model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2640, "dossier_id": "ubc/arat5/arat5v2-base-1024", "name": "AraT5v2-base-1024", "maker": "ubc", "family": "ubc/arat5", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2641, "dossier_id": "ubc/cheetah/cheetah-base", "name": "Cheetah-base", "maker": "ubc", "family": "ubc/cheetah", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2642, "dossier_id": "ucla/newsbart/newsbart-base", "name": "NewsBART base", "maker": "ucla", "family": "ucla/newsbart", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the newsbart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2643, "dossier_id": "ucla/scibart/scibart-base", "name": "SciBART base", "maker": "ucla", "family": "ucla/scibart", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the scibart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2644, "dossier_id": "ucla/scibart/scibart-large", "name": "SciBART large", "maker": "ucla", "family": "ucla/scibart", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the scibart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2645, "dossier_id": "ucsc/spikegpt/spikegpt-bookcorpus", "name": "SpikeGPT BookCorpus", "maker": "ucsc", "family": "ucsc/spikegpt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2646, "dossier_id": "ucsc/spikegpt/spikegpt-openwebtext-216m", "name": "SpikeGPT OpenWebText 216M", "maker": "ucsc", "family": "ucsc/spikegpt", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2647, "dossier_id": "udel/synteract/synteract", "name": "SYNTERACT", "maker": "udel", "family": "udel/synteract", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2648, "dossier_id": "unist/gmoe/gmoe-base", "name": "GMoE Base", "maker": "unist", "family": "unist/gmoe", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2649, "dossier_id": "unist/gmoe/gmoe-large", "name": "GMoE Large", "maker": "unist", "family": "unist/gmoe", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2650, "dossier_id": "unist/gmoe/gmoe-small", "name": "GMoE Small", "maker": "unist", "family": "unist/gmoe", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2651, "dossier_id": "waseda/comet/comet-gpt2-small-japanese", "name": "COMET-GPT2 ja", "maker": "waseda", "family": "waseda/comet", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2652, "dossier_id": "waseda/comet/comet-gpt2-xl-japanese", "name": "COMET-GPT2 ja XL", "maker": "waseda", "family": "waseda/comet", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2653, "dossier_id": "waseda/comet/comet-v2-gpt2-small-japanese", "name": "COMET-GPT2 ja v2", "maker": "waseda", "family": "waseda/comet", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2654, "dossier_id": "waseda/gpt2/gpt2-small-japanese", "name": "GPT-2 small Japanese", "maker": "waseda", "family": "waseda/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2655, "dossier_id": "waseda/gpt2/gpt2-small-japanese-wikipedia", "name": "GPT-2 small Japanese (Wikipedia)", "maker": "waseda", "family": "waseda/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2656, "dossier_id": "waseda/gpt2/gpt2-xl-japanese", "name": "GPT-2 XL Japanese", "maker": "waseda", "family": "waseda/gpt2", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2657, "dossier_id": "waseda/tacomet/tacomet-gpt2-xl-english", "name": "TaCOMET en", "maker": "waseda", "family": "waseda/tacomet", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2658, "dossier_id": "waseda/tacomet/tacomet-gpt2-xl-japanese", "name": "TaCOMET ja", "maker": "waseda", "family": "waseda/tacomet", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2659, "dossier_id": "yandex/yalm/yalm-100b", "name": "YaLM 100B", "maker": "yandex", "family": "yandex/yalm", "context_length": 1024, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research generation. Not an AI Studio endpoint and not the weights behind YandexGPT.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2660, "dossier_id": "rwkv/rwkv-2/pile", "name": "RWKV-2 Pile", "maker": "rwkv", "family": "rwkv/rwkv-2", "context_length": 768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2661, "dossier_id": "saarland/smole/smole-bert", "name": "Smole-BERT", "maker": "saarland", "family": "saarland/smole", "context_length": 768, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2662, "dossier_id": "antwerp/contact/contact", "name": "CoNTACT", "maker": "antwerp", "family": "antwerp/contact", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2663, "dossier_id": "antwerp/e5-nl/e5-base-trm", "name": "E5-base-trm", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2664, "dossier_id": "antwerp/e5-nl/e5-base-trm-nl", "name": "E5-base-trm-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2665, "dossier_id": "antwerp/e5-nl/e5-large-trm", "name": "E5-large-trm", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2666, "dossier_id": "antwerp/e5-nl/e5-large-trm-nl", "name": "E5-large-trm-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2667, "dossier_id": "aveiro/multi-head-crf/multi-head-crf", "name": "Multi-Head CRF", "maker": "aveiro", "family": "aveiro/multi-head-crf", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2668, "dossier_id": "aveiro/multiclinner/multiclinner-cz", "name": "MultiClinNER Czech", "maker": "aveiro", "family": "aveiro/multiclinner", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2669, "dossier_id": "aveiro/multiclinner/multiclinner-es", "name": "MultiClinNER Spanish", "maker": "aveiro", "family": "aveiro/multiclinner", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2670, "dossier_id": "aveiro/multiclinner/multiclinner-mixed", "name": "MultiClinNER Mixed", "maker": "aveiro", "family": "aveiro/multiclinner", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2671, "dossier_id": "aveiro/multiclinner/multiclinner-nl", "name": "MultiClinNER Dutch", "maker": "aveiro", "family": "aveiro/multiclinner", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2672, "dossier_id": "bar-ilan/abstract-sim/abstract-sim-query", "name": "Abstract-Sim query", "maker": "bar-ilan", "family": "bar-ilan/abstract-sim", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Query encoder for abstract descriptions. The card says to use it beside the sentence encoder.", "summary_intro": "Abstract-Sim query is one encoder from the Bar-Ilan NLP lab Abstract-Sim dual-encoder release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2673, "dossier_id": "bar-ilan/abstract-sim/abstract-sim-sentence", "name": "Abstract-Sim sentence", "maker": "bar-ilan", "family": "bar-ilan/abstract-sim", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence encoder. The card says to use it beside the query encoder.", "summary_intro": "Abstract-Sim sentence is one encoder from the Bar-Ilan NLP lab Abstract-Sim dual-encoder release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2674, "dossier_id": "bar-ilan/f-coref/f-coref", "name": "F-Coref", "maker": "bar-ilan", "family": "bar-ilan/f-coref", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster coreference than LingMess, with a smaller RoBERTa and a batching method the card calls leftover.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2675, "dossier_id": "bar-ilan/superpal/superpal", "name": "SuperPAL", "maker": "bar-ilan", "family": "bar-ilan/superpal", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Decide whether a summary proposition aligns with a source proposition.", "summary_intro": "SuperPAL is a Bar-Ilan NLP lab RoBERTa classifier for aligning summary propositions with source propositions. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2676, "dossier_id": "ghent/biolord/biolord-2022", "name": "BioLORD-2022", "maker": "ghent", "family": "ghent/biolord", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2677, "dossier_id": "ghent/biolord/biolord-2023", "name": "BioLORD-2023", "maker": "ghent", "family": "ghent/biolord", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2678, "dossier_id": "ghent/biolord/biolord-2023-m", "name": "BioLORD-2023-M", "maker": "ghent", "family": "ghent/biolord", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2679, "dossier_id": "ghent/biolord/biolord-2023-m-dutch-incontext", "name": "BioLORD-2023-M Dutch InContext v1", "maker": "ghent", "family": "ghent/biolord", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2680, "dossier_id": "heriot-watt/gbv/gbv-classifier-roberta-base", "name": "GBV Classifier RoBERTa-base", "maker": "heriot-watt", "family": "heriot-watt/gbv", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2681, "dossier_id": "heriot-watt/gbv/gbv-classifier-roberta-base-instruct", "name": "GBV Classifier RoBERTa-base Instruct", "maker": "heriot-watt", "family": "heriot-watt/gbv", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2682, "dossier_id": "huggingface/distilbert/distilroberta-base", "name": "DistilRoBERTa base", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face's distilled RoBERTa-base encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2683, "dossier_id": "ibm/granite-embedding/granite-embedding-107m-multilingual", "name": "granite-embedding-107m-multilingual", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2684, "dossier_id": "ibm/granite-embedding/granite-embedding-125m-english", "name": "granite-embedding-125m-english", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2685, "dossier_id": "ibm/granite-embedding/granite-embedding-278m-multilingual", "name": "granite-embedding-278m-multilingual", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 514, "prompt_price_per_m": 0.106, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2686, "dossier_id": "ibm/granite-embedding/granite-embedding-30m-english", "name": "Granite-Embedding-30m-English (revision r1.1)", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2687, "dossier_id": "ibm/granite-embedding/granite-embedding-30m-sparse", "name": "Granite-Embedding-30m-Sparse", "maker": "ibm", "family": "ibm/granite-embedding", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2688, "dossier_id": "ibm/granite-guardian/granite-guardian-hap-125m", "name": "Granite Guardian HAP 125M", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2689, "dossier_id": "ibm/granite-guardian/granite-guardian-hap-38m", "name": "Granite Guardian HAP 38M", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2690, "dossier_id": "inria/camembert/camembert-base", "name": "CamemBERT base", "maker": "inria", "family": "inria/camembert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2691, "dossier_id": "inria/camembert/camembert-base-ccnet", "name": "CamemBERT base CCNet", "maker": "inria", "family": "inria/camembert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2692, "dossier_id": "inria/camembert/camembert-base-ccnet-4gb", "name": "CamemBERT base CCNet 4GB", "maker": "inria", "family": "inria/camembert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2693, "dossier_id": "inria/camembert/camembert-base-oscar-4gb", "name": "CamemBERT base OSCAR 4GB", "maker": "inria", "family": "inria/camembert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2694, "dossier_id": "inria/camembert/camembert-base-wikipedia-4gb", "name": "CamemBERT base Wikipedia 4GB", "maker": "inria", "family": "inria/camembert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2695, "dossier_id": "inria/camembert/camembert-large", "name": "CamemBERT large", "maker": "inria", "family": "inria/camembert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2696, "dossier_id": "inria/camembert-bio/camembert-bio-base", "name": "CamemBERT-bio base", "maker": "inria", "family": "inria/camembert-bio", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2697, "dossier_id": "inria/dalembert/dalembert", "name": "D'AlemBERT", "maker": "inria", "family": "inria/dalembert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2698, "dossier_id": "leuven/robbert/robbert-2022", "name": "RobBERT-2022", "maker": "leuven", "family": "leuven/robbert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Dutch fill-mask and a fine-tuning base for classification, regression, and tagging, aimed at language use through 2022.", "summary_intro": "KU Leuven model RobBERT-2022 (robbert-2022). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2699, "dossier_id": "leuven/robbert/robbert-2023-base", "name": "RobBERT-2023 base", "maker": "leuven", "family": "leuven/robbert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "KU Leuven model RobBERT-2023 base (robbert-2023-base). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2700, "dossier_id": "leuven/robbert/robbert-2023-large", "name": "RobBERT-2023 large", "maker": "leuven", "family": "leuven/robbert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "KU Leuven model RobBERT-2023 large (robbert-2023-large). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2701, "dossier_id": "leuven/robbert/robbert-v2", "name": "RobBERT v2", "maker": "leuven", "family": "leuven/robbert", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "KU Leuven model RobBERT v2 (robbert-v2). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2702, "dossier_id": "leuven/robbertje/robbertje-1-gb-bort", "name": "RobBERTje 1GB BORT", "maker": "leuven", "family": "leuven/robbertje", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The compact end of the RobBERTje line for Dutch fine-tuning when the 74M distillations are still too large.", "summary_intro": "KU Leuven model RobBERTje 1GB BORT (robbertje-1-gb-bort). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2703, "dossier_id": "leuven/robbertje/robbertje-1-gb-merged", "name": "RobBERTje 1GB merged", "maker": "leuven", "family": "leuven/robbertje", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The RobBERTje variant the yearly cards point to when a deployment wants a smaller, faster Dutch RobBERT.", "summary_intro": "KU Leuven model RobBERTje 1GB merged (robbertje-1-gb-merged). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2704, "dossier_id": "ljubljana/sleng/sleng-bert", "name": "SlEng-bert", "maker": "ljubljana", "family": "ljubljana/sleng", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked language modeling on conversational Slovene and English.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2705, "dossier_id": "ljubljana/sloberta/sloberta-sleng", "name": "SloBERTa-SlEng", "maker": "ljubljana", "family": "ljubljana/sloberta", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked language modeling on conversational Slovene and English, including non-standard spelling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2706, "dossier_id": "lmu/glot500/glot500", "name": "Glot500", "maker": "lmu", "family": "lmu/glot500", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2707, "dossier_id": "nyu/miniberta/miniberta-base-100m", "name": "MiniBERTa base 100M", "maker": "nyu", "family": "nyu/miniberta", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2708, "dossier_id": "pisa/bureauberto/bureauberto", "name": "BureauBERTo", "maker": "pisa", "family": "pisa/bureauberto", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2709, "dossier_id": "rinna/roberta/japanese-roberta-base", "name": "Japanese RoBERTa Base", "maker": "rinna", "family": "rinna/roberta", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base Japanese RoBERTa for masked language modeling, trained by rinna on Japanese CC-100 and Japanese Wikipedia.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2710, "dossier_id": "tohoku/roberta/roberta-base-japanese", "name": "RoBERTa base Japanese", "maker": "tohoku", "family": "tohoku/roberta", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2711, "dossier_id": "ucas/jtcse/jtcse-roberta", "name": "JTCSE-RoBERTa", "maker": "ucas", "family": "ucas/jtcse", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2712, "dossier_id": "ucas/jtcse/jtcse-roberta-d", "name": "JTCSE-RoBERTa-D", "maker": "ucas", "family": "ucas/jtcse", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2713, "dossier_id": "ucas/jtcse/jtcse-roberta-ucd", "name": "JTCSE-RoBERTa-UCD", "maker": "ucas", "family": "ucas/jtcse", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2714, "dossier_id": "ucas/tncse/tncse-roberta", "name": "TNCSE-RoBERTa", "maker": "ucas", "family": "ucas/tncse", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2715, "dossier_id": "ucas/tncse/tncse-roberta-d", "name": "TNCSE-RoBERTa-D", "maker": "ucas", "family": "ucas/tncse", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2716, "dossier_id": "ucas/tncse/tncse-roberta-ucd", "name": "TNCSE-RoBERTa-UCD", "maker": "ucas", "family": "ucas/tncse", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2717, "dossier_id": "umn/scholawrite/scholawrite-roberta-classifier", "name": "ScholaWrite RoBERTa classifier", "maker": "umn", "family": "umn/scholawrite", "context_length": 514, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2718, "dossier_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", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2719, "dossier_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", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2720, "dossier_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", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2721, "dossier_id": "aisingapore/sea-embedding/sea-lion-e5-embedding-600m", "name": "SEA-LION-E5-Embedding-600M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval embeddings, as an alternative to the ModernBERT embedding models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2722, "dossier_id": "antwerp/e5-nl/e5-base-v2-t2t", "name": "E5-base-v2-t2t", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2723, "dossier_id": "antwerp/e5-nl/e5-base-v2-t2t-nl", "name": "E5-base-v2-t2t-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2724, "dossier_id": "antwerp/e5-nl/e5-large-v2-t2t", "name": "E5-large-v2-t2t", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2725, "dossier_id": "antwerp/e5-nl/e5-large-v2-t2t-nl", "name": "E5-large-v2-t2t-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2726, "dossier_id": "antwerp/e5-nl/e5-small-trm", "name": "E5-small-trm", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2727, "dossier_id": "antwerp/e5-nl/e5-small-trm-nl", "name": "E5-small-trm-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2728, "dossier_id": "antwerp/e5-nl/e5-small-v2-t2t", "name": "E5-small-v2-t2t", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2729, "dossier_id": "antwerp/e5-nl/e5-small-v2-t2t-nl", "name": "E5-small-v2-t2t-nl", "maker": "antwerp", "family": "antwerp/e5-nl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2730, "dossier_id": "antwerp/republic/republic", "name": "RePublic", "maker": "antwerp", "family": "antwerp/republic", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2731, "dossier_id": "athens/distilgreek/distilgreek-bert", "name": "DistilGREEK-BERT", "maker": "athens", "family": "athens/distilgreek", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2732, "dossier_id": "athens/greek-deberta/greek-deberta-base", "name": "GreekDeBERTa-base", "maker": "athens", "family": "athens/greek-deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2733, "dossier_id": "athens/greek-deberta/greek-deberta-v3-base", "name": "GreekDeBERTaV3-base", "maker": "athens", "family": "athens/greek-deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2734, "dossier_id": "athens/greek-deberta/greek-deberta-v3-xsmall", "name": "GreekDeBERTaV3-xsmall", "maker": "athens", "family": "athens/greek-deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2735, "dossier_id": "athens/greek-legal-bert/greek-legal-bert", "name": "GreekLegalBERT", "maker": "athens", "family": "athens/greek-legal-bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2736, "dossier_id": "athens/greek-legal-bert/greek-legal-bert-v2", "name": "GreekLegalBERT v2", "maker": "athens", "family": "athens/greek-legal-bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2737, "dossier_id": "athens/greek-legal-roberta/greek-legal-roberta-v2", "name": "GreekLegalRoBERTa v2", "maker": "athens", "family": "athens/greek-legal-roberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2738, "dossier_id": "athens/greek-legal-roberta/greek-legal-roberta-v3", "name": "GreekLegalRoBERTa v3", "maker": "athens", "family": "athens/greek-legal-roberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2739, "dossier_id": "athens/greek-legal-roberta/greek-legal-roberta-v4", "name": "GreekLegalRoBERTa v4", "maker": "athens", "family": "athens/greek-legal-roberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2740, "dossier_id": "aveiro/bionext/bionext-extractor", "name": "BioNExt Extractor", "maker": "aveiro", "family": "aveiro/bionext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2741, "dossier_id": "aveiro/bionext/bionext-tagger", "name": "BioNExt Tagger", "maker": "aveiro", "family": "aveiro/bionext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2742, "dossier_id": "aveiro/multiclinner/multiclinner-en", "name": "MultiClinNER English", "maker": "aveiro", "family": "aveiro/multiclinner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2743, "dossier_id": "aveiro/multiclinner/multiclinner-it", "name": "MultiClinNER Italian", "maker": "aveiro", "family": "aveiro/multiclinner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2744, "dossier_id": "aveiro/multiclinner/multiclinner-ro", "name": "MultiClinNER Romanian", "maker": "aveiro", "family": "aveiro/multiclinner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2745, "dossier_id": "aveiro/multiclinner/multiclinner-sv", "name": "MultiClinNER Swedish", "maker": "aveiro", "family": "aveiro/multiclinner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2746, "dossier_id": "baai/bge/bge-base-en", "name": "bge-base-en", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2747, "dossier_id": "baai/bge/bge-base-en-v1-5", "name": "bge-base-en-v1.5", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2748, "dossier_id": "baai/bge/bge-base-zh", "name": "bge-base-zh", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2749, "dossier_id": "baai/bge/bge-base-zh-v1-5", "name": "bge-base-zh-v1.5", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2750, "dossier_id": "baai/bge/bge-large-en", "name": "bge-large-en", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2751, "dossier_id": "baai/bge/bge-large-en-v1-5", "name": "bge-large-en-v1.5", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2752, "dossier_id": "baai/bge/bge-large-zh", "name": "bge-large-zh", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2753, "dossier_id": "baai/bge/bge-large-zh-noinstruct", "name": "bge-large-zh-noinstruct", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2754, "dossier_id": "baai/bge/bge-large-zh-v1-5", "name": "bge-large-zh-v1.5", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2755, "dossier_id": "baai/bge/bge-small-en", "name": "bge-small-en", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2756, "dossier_id": "baai/bge/bge-small-en-v1-5", "name": "bge-small-en-v1.5", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2757, "dossier_id": "baai/bge/bge-small-zh", "name": "bge-small-zh", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2758, "dossier_id": "baai/bge/bge-small-zh-v1-5", "name": "bge-small-zh-v1.5", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2759, "dossier_id": "baai/bge/llm-embedder", "name": "LLM-Embedder", "maker": "baai", "family": "baai/bge", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2760, "dossier_id": "baai/reranker/bge-reranker-base", "name": "bge-reranker-base", "maker": "baai", "family": "baai/reranker", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Re-rank the top documents returned by a bi-encoder. More accurate than the embedding model and more expensive.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2761, "dossier_id": "baai/reranker/bge-reranker-large", "name": "bge-reranker-large", "maker": "baai", "family": "baai/reranker", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same rerank job as bge-reranker-base, with a large XLM-RoBERTa backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2762, "dossier_id": "baichuan/embedding/baichuan-text-embedding", "name": "Baichuan-Text-Embedding", "maker": "baichuan", "family": "baichuan/embedding", "context_length": 512, "prompt_price_per_m": 0.0005, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2763, "dossier_id": "bar-ilan/abstract-sim/abstract-sim-query-pubmed", "name": "Abstract-Sim query PubMed", "maker": "bar-ilan", "family": "bar-ilan/abstract-sim", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "PubMed query encoder, paired with the PubMed sentence encoder. The sample pins revision 8d34676d80a39bcbc5a1d2eec13e6f8078496215 for this repo.", "summary_intro": "Abstract-Sim query PubMed is one encoder from the Bar-Ilan NLP lab Abstract-Sim dual-encoder release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2764, "dossier_id": "bar-ilan/abstract-sim/abstract-sim-sentence-pubmed", "name": "Abstract-Sim sentence PubMed", "maker": "bar-ilan", "family": "bar-ilan/abstract-sim", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "PubMed sentence encoder, paired with the PubMed query encoder. The sample pins two revision ids.", "summary_intro": "Abstract-Sim sentence PubMed is one encoder from the Bar-Ilan NLP lab Abstract-Sim dual-encoder release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2765, "dossier_id": "bar-ilan/alephbert/alephbert", "name": "AlephBERT", "maker": "bar-ilan", "family": "bar-ilan/alephbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2766, "dossier_id": "bar-ilan/alephbertgimmel/alephbertgimmel", "name": "AlephBERTGimmel", "maker": "bar-ilan", "family": "bar-ilan/alephbertgimmel", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2767, "dossier_id": "bar-ilan/alephbertgimmel/alephbertgimmel-small", "name": "AlephBERTGimmel-small", "maker": "bar-ilan", "family": "bar-ilan/alephbertgimmel", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card cites the AlephBERTGimmel paper. It does not separately describe a task.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2768, "dossier_id": "bar-ilan/berel/berel", "name": "BEREL", "maker": "bar-ilan", "family": "bar-ilan/berel", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2769, "dossier_id": "bar-ilan/berel/berel-3-0", "name": "BEREL 3.0", "maker": "bar-ilan", "family": "bar-ilan/berel", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked language modeling for rabbinic Hebrew. The card does not add a task list beyond the original BEREL citation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2770, "dossier_id": "bar-ilan/berel/berel-seg", "name": "BEREL-seg", "maker": "bar-ilan", "family": "bar-ilan/berel", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Prefix or morpheme segmentation of rabbinic Hebrew so Pre-BEREL can see segmented tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2771, "dossier_id": "bar-ilan/berel/pre-berel", "name": "Pre-BEREL", "maker": "bar-ilan", "family": "bar-ilan/berel", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked language modeling over morphologically segmented rabbinic Hebrew. The card says segmentation is a step toward morphology-aware tokenization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2772, "dossier_id": "bar-ilan/dictabert/dictabert", "name": "DictaBERT", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2773, "dossier_id": "bar-ilan/dictabert/dictabert-heq", "name": "DictaBERT-HeQ", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fine-tuned extractive question-answering model on HeQ. The card sample uses the question-answering pipeline.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2774, "dossier_id": "bar-ilan/dictabert/dictabert-joint", "name": "DictaBERT-joint", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-task model. The card lists prefix segmentation, morphological disambiguation, lemmatization, dependency trees, and NER.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2775, "dossier_id": "bar-ilan/dictabert/dictabert-large", "name": "DictaBERT-large", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "BERT-large masked-language model for modern Hebrew.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2776, "dossier_id": "bar-ilan/dictabert/dictabert-large-heq", "name": "DictaBERT-large-HeQ", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "BERT-large question-answering model for Hebrew.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2777, "dossier_id": "bar-ilan/dictabert/dictabert-large-ner", "name": "DictaBERT-large-NER", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "BERT-large token-classification NER model for Hebrew.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2778, "dossier_id": "bar-ilan/dictabert/dictabert-large-parse", "name": "DictaBERT-large-parse", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2779, "dossier_id": "bar-ilan/dictabert/dictabert-lex", "name": "DictaBERT-lex", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lemmatization model. The card says the lemma of a verb is the third-person past singular.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2780, "dossier_id": "bar-ilan/dictabert/dictabert-morph", "name": "DictaBERT-morph", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fine-tuned head for Hebrew morphological tagging.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2781, "dossier_id": "bar-ilan/dictabert/dictabert-ner", "name": "DictaBERT-NER", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Token-classification NER model. The card sample uses the ner pipeline with aggregation_strategy simple.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2782, "dossier_id": "bar-ilan/dictabert/dictabert-parse", "name": "DictaBERT-parse", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2783, "dossier_id": "bar-ilan/dictabert/dictabert-seg", "name": "DictaBERT-seg", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fine-tuned head for Hebrew prefix segmentation. The paper says one Hub call returns the segmentation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2784, "dossier_id": "bar-ilan/dictabert/dictabert-sentiment", "name": "DictaBERT-sentiment", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentiment classifier. The card names the HebArabNlpProject/HebrewSentiment dataset and the sentiment-analysis pipeline.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2785, "dossier_id": "bar-ilan/dictabert/dictabert-splinter", "name": "DictaBERT-splinter", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hebrew BERT trained for splinter tokenization of nonconcatenative morphology. The card says to load the tokenizer with trust_remote_code.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2786, "dossier_id": "bar-ilan/dictabert/dictabert-syntax", "name": "DictaBERT-syntax", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Dependency syntax model. Custom code. The card sample calls a predict-style output.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2787, "dossier_id": "bar-ilan/dictabert/dictabert-tiny", "name": "DictaBERT-tiny", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller DictaBERT masked-language model. The parsing paper points at this repo as the tiny foundation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2788, "dossier_id": "bar-ilan/dictabert/dictabert-tiny-joint", "name": "DictaBERT-tiny-joint", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tiny multi-task model with the same five-task list as DictaBERT-joint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2789, "dossier_id": "bar-ilan/dictabert/dictabert-tiny-parse", "name": "DictaBERT-tiny-parse", "maker": "bar-ilan", "family": "bar-ilan/dictabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tiny parser from the MRL parsing paper, for lower memory.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2790, "dossier_id": "bar-ilan/msbert/msbert", "name": "MsBERT", "maker": "bar-ilan", "family": "bar-ilan/msbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it predicts missing words in fragmentary manuscript transcriptions and helps separate quoted passages from exegetical text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2791, "dossier_id": "bar-ilan/otobert/otobert", "name": "OtoBERT", "maker": "bar-ilan", "family": "bar-ilan/otobert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it identifies suffixed verbal forms in modern Hebrew literature.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2792, "dossier_id": "bar-ilan/qanom/nominalization-candidate-classifier", "name": "Nominalization candidate classifier", "maker": "bar-ilan", "family": "bar-ilan/qanom", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repo name says it classifies nominalization candidates. No usage text was published on the repo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2793, "dossier_id": "bar-ilan/qanom/qanom-seq2seq-joint", "name": "QANom seq2seq joint", "maker": "bar-ilan", "family": "bar-ilan/qanom", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repo name says a joint sequence-to-sequence QANom model. The task definition was not on the Hub page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2794, "dossier_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", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Summarize a passage so the summary covers all and only the marked highlight spans.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2795, "dossier_id": "bari/alberto/alberto", "name": "AlBERTo", "maker": "bari", "family": "bari/alberto", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2796, "dossier_id": "basque/eriberta/eriberta", "name": "EriBERTa", "maker": "basque", "family": "basque/eriberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2797, "dossier_id": "basque/pl-bert/pl-bert-eu", "name": "PL-BERT-eu", "maker": "basque", "family": "basque/pl-bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2798, "dossier_id": "cnrs/drbert/drbert-4gb", "name": "DrBERT-4GB", "maker": "cnrs", "family": "cnrs/drbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2799, "dossier_id": "cnrs/drbert/drbert-7gb", "name": "DrBERT-7GB", "maker": "cnrs", "family": "cnrs/drbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2800, "dossier_id": "cnrs/drbert/drbert-7gb-large", "name": "DrBERT-7GB-Large", "maker": "cnrs", "family": "cnrs/drbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2801, "dossier_id": "cnrs/flaubert/flaubert-base-cased", "name": "FlauBERT base cased", "maker": "cnrs", "family": "cnrs/flaubert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2802, "dossier_id": "cnrs/flaubert/flaubert-base-uncased", "name": "FlauBERT base uncased", "maker": "cnrs", "family": "cnrs/flaubert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2803, "dossier_id": "cnrs/flaubert/flaubert-large-cased", "name": "FlauBERT large cased", "maker": "cnrs", "family": "cnrs/flaubert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2804, "dossier_id": "cnrs/flaubert/flaubert-small-cased", "name": "FlauBERT small cased", "maker": "cnrs", "family": "cnrs/flaubert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2805, "dossier_id": "cnrs/jargon/jargon-biomed", "name": "Jargon biomed", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2806, "dossier_id": "cnrs/jargon/jargon-general-base", "name": "Jargon general base", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2807, "dossier_id": "cnrs/jargon/jargon-general-biomed", "name": "Jargon general biomed", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2808, "dossier_id": "cnrs/jargon/jargon-general-legal", "name": "Jargon general legal", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2809, "dossier_id": "cnrs/jargon/jargon-legal", "name": "Jargon legal", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2810, "dossier_id": "cnrs/jargon/jargon-multidomain-base", "name": "Jargon multidomain base", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2811, "dossier_id": "cnrs/jargon/jargon-nachos", "name": "Jargon NACHOS", "maker": "cnrs", "family": "cnrs/jargon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2812, "dossier_id": "cohere/embed/embed-english-light-v3-0", "name": "Embed English Light v3", "maker": "cohere", "family": "cohere/embed", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2813, "dossier_id": "cohere/embed/embed-english-v3-0", "name": "Embed English v3", "maker": "cohere", "family": "cohere/embed", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2814, "dossier_id": "cohere/embed/embed-multilingual-light-v3-0", "name": "Embed Multilingual Light v3", "maker": "cohere", "family": "cohere/embed", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2815, "dossier_id": "cohere/embed/embed-multilingual-v3-0", "name": "Embed Multilingual v3", "maker": "cohere", "family": "cohere/embed", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2816, "dossier_id": "concordia/wavslm/wavslm-2k", "name": "WavSLM-2k", "maker": "concordia", "family": "concordia/wavslm", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2817, "dossier_id": "concordia/wavslm/wavslm-4k", "name": "WavSLM-4k", "maker": "concordia", "family": "concordia/wavslm", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2818, "dossier_id": "concordia/wavslm/wavslm-65k", "name": "WavSLM-65k", "maker": "concordia", "family": "concordia/wavslm", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2819, "dossier_id": "cuni/eleczech/eleczech-lc-small", "name": "EleCzech-LC-small", "maker": "cuni", "family": "cuni/eleczech", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents it as a lowercased-Czech language representation model trained on the same collection as RobeCzech.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2820, "dossier_id": "cuni/robeczech/robeczech-base", "name": "RobeCzech", "maker": "cuni", "family": "cuni/robeczech", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2821, "dossier_id": "gothenburg/attitude/kb-bert-posneg-attitude", "name": "KB-BERT Swedish pos/neg attitude", "maker": "gothenburg", "family": "gothenburg/attitude", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2822, "dossier_id": "gothenburg/cefr/kb-bert-swedish-cefr", "name": "KB-BERT Swedish CEFR classification", "maker": "gothenburg", "family": "gothenburg/cefr", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2823, "dossier_id": "gothenburg/emotions/kb-bert-emotions-brighter", "name": "KB-BERT Swedish BRIGHTER emotions", "maker": "gothenburg", "family": "gothenburg/emotions", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2824, "dossier_id": "gothenburg/pi-detection/kb-bert-pi-basic", "name": "KB-BERT Swedish PI detection (basic)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2825, "dossier_id": "gothenburg/pi-detection/kb-bert-pi-basic-iob", "name": "KB-BERT Swedish PI detection (basic IOB)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2826, "dossier_id": "gothenburg/pi-detection/kb-bert-pi-detailed", "name": "KB-BERT Swedish PI detection (detailed)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2827, "dossier_id": "gothenburg/pi-detection/kb-bert-pi-detailed-iob", "name": "KB-BERT Swedish PI detection (detailed IOB)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2828, "dossier_id": "gothenburg/pi-detection/kb-bert-pi-general", "name": "KB-BERT Swedish PI detection (general)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2829, "dossier_id": "gothenburg/pi-detection/kb-bert-pi-general-iob", "name": "KB-BERT Swedish PI detection (general IOB)", "maker": "gothenburg", "family": "gothenburg/pi-detection", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2830, "dossier_id": "groningen/bertje/bertje", "name": "BERTje", "maker": "groningen", "family": "groningen/bertje", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2831, "dossier_id": "groningen/bertje/bertje-frisian", "name": "BERTje Frisian", "maker": "groningen", "family": "groningen/bertje", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2832, "dossier_id": "groningen/bertje/bertje-gronings", "name": "BERTje Gronings", "maker": "groningen", "family": "groningen/bertje", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2833, "dossier_id": "groningen/hatebert/hatebert", "name": "HateBERT", "maker": "groningen", "family": "groningen/hatebert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2834, "dossier_id": "guelph/barcodebert/barcodebert", "name": "BarcodeBERT", "maker": "guelph", "family": "guelph/barcodebert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2835, "dossier_id": "haifa/knesset/knesset-dicta-checkworthiness", "name": "knesset-dicta-checkworthiness", "maker": "haifa", "family": "haifa/knesset", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2836, "dossier_id": "haifa/knesset/knesset-dictabert", "name": "Knesset-DictaBERT", "maker": "haifa", "family": "haifa/knesset", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2837, "dossier_id": "haifa/knesset/knesset-multi-e5-large", "name": "Knesset-multi-e5-large", "maker": "haifa", "family": "haifa/knesset", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2838, "dossier_id": "hit/hfl/chinese-bert-wwm", "name": "Chinese BERT-wwm", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2839, "dossier_id": "hit/hfl/chinese-bert-wwm-ext", "name": "Chinese BERT-wwm-ext", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same masking idea as BERT-wwm, with more pretraining text. The README lists 5.4B words for EXT data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2840, "dossier_id": "hit/hfl/chinese-electra-180g-base", "name": "Chinese ELECTRA-180g-base", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2841, "dossier_id": "hit/hfl/chinese-electra-180g-large", "name": "Chinese ELECTRA-180g-large", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2842, "dossier_id": "hit/hfl/chinese-electra-180g-small", "name": "Chinese ELECTRA-180g-small", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2843, "dossier_id": "hit/hfl/chinese-electra-180g-small-ex", "name": "Chinese ELECTRA-180g-small-ex", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2844, "dossier_id": "hit/hfl/chinese-electra-base", "name": "Chinese ELECTRA-base", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2845, "dossier_id": "hit/hfl/chinese-electra-large", "name": "Chinese ELECTRA-large", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2846, "dossier_id": "hit/hfl/chinese-electra-small", "name": "Chinese ELECTRA-small", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2847, "dossier_id": "hit/hfl/chinese-electra-small-ex", "name": "Chinese ELECTRA-small-ex", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2848, "dossier_id": "hit/hfl/chinese-legal-electra-base", "name": "Chinese Legal ELECTRA-base", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2849, "dossier_id": "hit/hfl/chinese-legal-electra-large", "name": "Chinese Legal ELECTRA-large", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2850, "dossier_id": "hit/hfl/chinese-legal-electra-small", "name": "Chinese Legal ELECTRA-small", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replaced-token detection pretraining for Chinese downstream tasks. The README points at Google's ELECTRA code as the base implementation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2851, "dossier_id": "hit/hfl/chinese-roberta-wwm-ext", "name": "Chinese RoBERTa-wwm-ext", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2852, "dossier_id": "hit/hfl/chinese-roberta-wwm-ext-large", "name": "Chinese RoBERTa-wwm-ext-large", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same training recipe as RoBERTa-wwm-ext at the large size. Load with Bert functions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2853, "dossier_id": "hit/hfl/chinese-xlnet-base", "name": "Chinese XLNet-base", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller Chinese XLNet for downstream fine-tuning. Hub pipeline is text-generation.", "summary_intro": "Chinese XLNet-base is the 12-layer HFL Chinese XLNet. Collected 2026-10-15. News line 2019-09-05. Apache-2.0.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2854, "dossier_id": "hit/hfl/chinese-xlnet-mid", "name": "Chinese XLNet-mid", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Permutation-language-model pretraining for Chinese, released before the base checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2855, "dossier_id": "hit/hfl/minirbt-h256", "name": "MiniRBT-H256", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2856, "dossier_id": "hit/hfl/minirbt-h288", "name": "MiniRBT-H288", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2857, "dossier_id": "hit/hfl/rbt4-h312", "name": "RBT4-H312", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "RBT4-H312 is the TinyBERT-sized comparison checkpoint HFL released beside MiniRBT. Collected 2026-10-15. Apache-2.0.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2858, "dossier_id": "hit/hfl/rbt6", "name": "RBT6", "maker": "hit", "family": "hit/hfl", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The MiniRBT README says MiniRBT models are distilled from this assistant, which itself is distilled from RoBERTa-wwm-ext.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2859, "dossier_id": "hitachi/bert/hitachi-bert", "name": "HitachiBERT", "maker": "hitachi", "family": "hitachi/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2860, "dossier_id": "hku/easyrec/easyrec-roberta-base", "name": "EasyRec-RoBERTa-Base", "maker": "hku", "family": "hku/easyrec", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2861, "dossier_id": "hku/easyrec/easyrec-roberta-large", "name": "EasyRec-RoBERTa-Large", "maker": "hku", "family": "hku/easyrec", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2862, "dossier_id": "hku/easyrec/easyrec-roberta-small", "name": "EasyRec-RoBERTa-Small", "maker": "hku", "family": "hku/easyrec", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2863, "dossier_id": "hku/instructor/instructor-base", "name": "Instructor-Base", "maker": "hku", "family": "hku/instructor", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2864, "dossier_id": "hku/instructor/instructor-large", "name": "Instructor-Large", "maker": "hku", "family": "hku/instructor", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2865, "dossier_id": "hku/instructor/instructor-xl", "name": "Instructor-XL", "maker": "hku", "family": "hku/instructor", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2866, "dossier_id": "huggingface/distilbert/distilbert-base-cased", "name": "DistilBERT base cased", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cased English DistilBERT encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2867, "dossier_id": "huggingface/distilbert/distilbert-base-german-cased", "name": "DistilBERT German cased", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "German cased DistilBERT published under the official distilbert org.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2868, "dossier_id": "huggingface/distilbert/distilbert-base-multilingual-cased", "name": "DistilBERT multilingual cased", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual cased DistilBERT.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2869, "dossier_id": "huggingface/distilbert/distilbert-base-uncased", "name": "DistilBERT base uncased", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The original English uncased DistilBERT encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2870, "dossier_id": "huggingface/distilbert/distilbert-squad-cased", "name": "DistilBERT SQuAD cased", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Official cased DistilBERT model for extractive question answering on SQuAD.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2871, "dossier_id": "huggingface/distilbert/distilbert-squad-uncased", "name": "DistilBERT SQuAD uncased", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Official uncased DistilBERT model for extractive question answering on SQuAD.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2872, "dossier_id": "huggingface/distilbert/distilbert-sst-2", "name": "DistilBERT SST-2", "maker": "huggingface", "family": "huggingface/distilbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Official English sentiment classifier built on DistilBERT base uncased.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2873, "dossier_id": "ibm/granite-timeseries/granite-timeseries-patchtsmixer", "name": "Granite Timeseries Patchtsmixer", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2874, "dossier_id": "ibm/granite-timeseries/granite-timeseries-patchtst", "name": "Granite Timeseries Patchtst", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2875, "dossier_id": "ibm/granite-timeseries/granite-timeseries-tspulse-r1", "name": "Granite-TimeSeries-TSPulse-R1 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2876, "dossier_id": "ibm/granite-timeseries/granite-timeseries-ttm-r1", "name": "Granite-TimeSeries-TTM-R1 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2877, "dossier_id": "ibm/granite-timeseries/granite-timeseries-ttm-r2", "name": "Granite-TimeSeries-TTM-R2 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 512, "prompt_price_per_m": 0.00013, "completion_price_per_m": 0.00038, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2878, "dossier_id": "ibm/granite-timeseries/granite-timeseries-ttm-r3", "name": "Granite-TTM-R3 Model Card", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2879, "dossier_id": "iitd/setu/setu-classifier-15m", "name": "RSL-SETU-Classifier-15M", "maker": "iitd", "family": "iitd/setu", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2880, "dossier_id": "iitkgp/inlegal/custominlawbert", "name": "CustomInLawBERT", "maker": "iitkgp", "family": "iitkgp/inlegal", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2881, "dossier_id": "iitkgp/inlegal/incaselawbert", "name": "InCaseLawBERT", "maker": "iitkgp", "family": "iitkgp/inlegal", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2882, "dossier_id": "iitkgp/inlegal/inlegalbert", "name": "InLegalBERT", "maker": "iitkgp", "family": "iitkgp/inlegal", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2883, "dossier_id": "inria/camemberta/camemberta-base", "name": "CamemBERTa base", "maker": "inria", "family": "inria/camemberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2884, "dossier_id": "inria/camemberta/camemberta-base-generator", "name": "CamemBERTa base generator", "maker": "inria", "family": "inria/camemberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2885, "dossier_id": "jhu/roberta-eng-ara/roberta-large-eng-ara-128k", "name": "RoBERTa-large English-Arabic 128K", "maker": "jhu", "family": "jhu/roberta-eng-ara", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2886, "dossier_id": "jina/embeddings/jina-embedding-b-en-v1", "name": "jina-embedding-b-en-v1", "maker": "jina", "family": "jina/embeddings", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First-generation English sentence embedding still listed on the 2026 index. Superseded by the v2 8K models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2887, "dossier_id": "jyvaskyla/finarcner/finarcner", "name": "FinArcNER", "maker": "jyvaskyla", "family": "jyvaskyla/finarcner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2888, "dossier_id": "jyvaskyla/l2-finnish/l2-finnish", "name": "L2 Finnish model", "maker": "jyvaskyla", "family": "jyvaskyla/l2-finnish", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2889, "dossier_id": "korea-university/kure/koe5", "name": "KoE5", "maker": "korea-university", "family": "korea-university/kure", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2890, "dossier_id": "kyoto/bert/bert-base-japanese-ssuw", "name": "Japanese BERT base SSUW", "maker": "kyoto", "family": "kyoto/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2891, "dossier_id": "kyoto/deberta/deberta-v2-base-japanese", "name": "Japanese DeBERTa V2 base", "maker": "kyoto", "family": "kyoto/deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2892, "dossier_id": "kyoto/deberta/deberta-v2-base-japanese-char-wwm", "name": "Japanese DeBERTa V2 base char WWM", "maker": "kyoto", "family": "kyoto/deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2893, "dossier_id": "kyoto/deberta/deberta-v2-large-japanese", "name": "Japanese DeBERTa V2 large", "maker": "kyoto", "family": "kyoto/deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2894, "dossier_id": "kyoto/deberta/deberta-v2-large-japanese-char-wwm", "name": "Japanese DeBERTa V2 large char WWM", "maker": "kyoto", "family": "kyoto/deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2895, "dossier_id": "kyoto/deberta/deberta-v2-tiny-japanese", "name": "Japanese DeBERTa V2 tiny", "maker": "kyoto", "family": "kyoto/deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2896, "dossier_id": "kyoto/deberta/deberta-v2-tiny-japanese-char-wwm", "name": "Japanese DeBERTa V2 tiny char WWM", "maker": "kyoto", "family": "kyoto/deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2897, "dossier_id": "kyoto/deberta/deberta-v3-base-japanese", "name": "Japanese DeBERTa V3 base", "maker": "kyoto", "family": "kyoto/deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2898, "dossier_id": "kyoto/roberta/roberta-base-japanese-char-wwm", "name": "Japanese RoBERTa base char WWM", "maker": "kyoto", "family": "kyoto/roberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2899, "dossier_id": "kyoto/roberta/roberta-base-japanese-ssuw", "name": "Japanese RoBERTa base SSUW", "maker": "kyoto", "family": "kyoto/roberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2900, "dossier_id": "kyoto/roberta/roberta-large-japanese-char-wwm", "name": "Japanese RoBERTa large char WWM", "maker": "kyoto", "family": "kyoto/roberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2901, "dossier_id": "lanzhou/tibetan/tbert", "name": "TBERT", "maker": "lanzhou", "family": "lanzhou/tibetan", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2902, "dossier_id": "lanzhou/tibetan/tibetan-bert-wwm", "name": "Tibetan-BERT-wwm", "maker": "lanzhou", "family": "lanzhou/tibetan", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2903, "dossier_id": "leiden/gysbert/gysbert", "name": "GysBERT", "maker": "leiden", "family": "leiden/gysbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2904, "dossier_id": "leiden/gysbert/gysbert-v2", "name": "GysBERT v2", "maker": "leiden", "family": "leiden/gysbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2905, "dossier_id": "leiden/macberth/macberth", "name": "MacBERTh", "maker": "leiden", "family": "leiden/macberth", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2906, "dossier_id": "leuven/robbert/robbert-2026", "name": "RobBERT-2026", "maker": "leuven", "family": "leuven/robbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A general Dutch masked LM and fine-tuning encoder for settings that need a permissibly licensed or synthetic pretraining corpus.", "summary_intro": "KU Leuven model RobBERT-2026 (robbert-2026). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2907, "dossier_id": "leuven/robbert/robbert-2026-retrieval", "name": "RobBERT-2026 Retrieval", "maker": "leuven", "family": "leuven/robbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot Dutch dense retrieval. The card requires query: and document: prefixes, CLS pooling, and cosine similarity.", "summary_intro": "KU Leuven model RobBERT-2026 Retrieval (robbert-2026-retrieval). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2908, "dossier_id": "line/distilbert/line-distilbert-base-japanese", "name": "LINE DistilBERT Japanese", "maker": "line", "family": "line/distilbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2909, "dossier_id": "liquid-ai/lfm2-5-nano/lfm2-5-embedding-350m", "name": "LFM2.5-Embedding-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2910, "dossier_id": "lisbon/albertina/albertina-100m-ptpt", "name": "Albertina 100M PT-PT", "maker": "lisbon", "family": "lisbon/albertina", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2911, "dossier_id": "lisbon/albertina/albertina-1b5-ptpt", "name": "Albertina 1.5B PT-PT", "maker": "lisbon", "family": "lisbon/albertina", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2912, "dossier_id": "lisbon/albertina/albertina-900m-ptpt", "name": "Albertina 900M PT-PT", "maker": "lisbon", "family": "lisbon/albertina", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2913, "dossier_id": "ljubljana/crosloengual/crosloengual-bert", "name": "CroSloEngual BERT", "maker": "ljubljana", "family": "ljubljana/crosloengual", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contextual embeddings and cross-lingual transfer among Croatian, Slovene, and English.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2914, "dossier_id": "ljubljana/sloberta/sloberta", "name": "SloBERTa", "maker": "ljubljana", "family": "ljubljana/sloberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contextual embeddings for Slovene classification, tagging, and other fine-tunes. It is a masked language model, not a chatbot.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2915, "dossier_id": "ljubljana/sloberta/sloberta-trendi-topics", "name": "SloBERTa-Trendi-Topics 1.0", "maker": "ljubljana", "family": "ljubljana/sloberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Thirteen-way topic labels for Slovene news.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2916, "dossier_id": "lund/easyner/biobert-bc5cdr-disease-v1", "name": "BioBERT BC5CDR disease v1", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2917, "dossier_id": "lund/easyner/biobert-bc5cdr-disease-v2", "name": "BioBERT BC5CDR disease v2", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2918, "dossier_id": "lund/easyner/biobert-huner-cell-v1", "name": "BioBERT HUNER cell v1", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2919, "dossier_id": "lund/easyner/biobert-huner-cell-v2", "name": "BioBERT HUNER cell v2", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2920, "dossier_id": "lund/easyner/biobert-huner-chemical-v1", "name": "BioBERT HUNER chemical v1", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2921, "dossier_id": "lund/easyner/biobert-huner-chemical-v2", "name": "BioBERT HUNER chemical v2", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2922, "dossier_id": "lund/easyner/biobert-huner-disease-v1", "name": "BioBERT HUNER disease v1", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2923, "dossier_id": "lund/easyner/biobert-huner-disease-v2", "name": "BioBERT HUNER disease v2", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2924, "dossier_id": "lund/easyner/biobert-huner-gene-v1", "name": "BioBERT HUNER gene v1", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2925, "dossier_id": "lund/easyner/biobert-huner-gene-v2", "name": "BioBERT HUNER gene v2", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2926, "dossier_id": "lund/easyner/biobert-huner-species-v1", "name": "BioBERT HUNER species v1", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2927, "dossier_id": "lund/easyner/biobert-huner-species-v2", "name": "BioBERT HUNER species v2", "maker": "lund", "family": "lund/easyner", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2928, "dossier_id": "maastricht/legal-camembert/legal-camembert-base", "name": "Legal-CamemBERT-base", "maker": "maastricht", "family": "maastricht/legal-camembert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2929, "dossier_id": "maastricht/legal-camembert/legal-distilcamembert", "name": "Legal-DistilCamemBERT-base", "maker": "maastricht", "family": "maastricht/legal-camembert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2930, "dossier_id": "manchester/fintext/fintext-chronos-mini-augmented", "name": "FinText Chronos-Mini (Augmented)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2931, "dossier_id": "manchester/fintext/fintext-chronos-mini-global", "name": "FinText Chronos-Mini (Global)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2932, "dossier_id": "manchester/fintext/fintext-chronos-mini-us", "name": "FinText Chronos-Mini (U.S.)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2933, "dossier_id": "manchester/fintext/fintext-chronos-small-augmented", "name": "FinText Chronos-Small (Augmented)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2934, "dossier_id": "manchester/fintext/fintext-chronos-small-global", "name": "FinText Chronos-Small (Global)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2935, "dossier_id": "manchester/fintext/fintext-chronos-small-us", "name": "FinText Chronos-Small (U.S.)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2936, "dossier_id": "manchester/fintext/fintext-chronos-tiny-augmented", "name": "FinText Chronos-Tiny (Augmented)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2937, "dossier_id": "manchester/fintext/fintext-chronos-tiny-global", "name": "FinText Chronos-Tiny (Global)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2938, "dossier_id": "manchester/fintext/fintext-chronos-tiny-us", "name": "FinText Chronos-Tiny (U.S.)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2939, "dossier_id": "manchester/fintext/fintext-timesfm-20m-augmented", "name": "FinText TimesFM-20M (Augmented)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2940, "dossier_id": "manchester/fintext/fintext-timesfm-20m-global", "name": "FinText TimesFM-20M (Global)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2941, "dossier_id": "manchester/fintext/fintext-timesfm-20m-us", "name": "FinText TimesFM-20M (U.S.)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2942, "dossier_id": "manchester/fintext/fintext-timesfm-8m-augmented", "name": "FinText TimesFM-8M (Augmented)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2943, "dossier_id": "manchester/fintext/fintext-timesfm-8m-global", "name": "FinText TimesFM-8M (Global)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2944, "dossier_id": "manchester/fintext/fintext-timesfm-8m-us", "name": "FinText TimesFM-8M (U.S.)", "maker": "manchester", "family": "manchester/fintext", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2945, "dossier_id": "mannheim/babelbert/babelbert-mbert", "name": "BabelBERT mBERT", "maker": "mannheim", "family": "mannheim/babelbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2946, "dossier_id": "mannheim/taser/taser", "name": "TaSeR", "maker": "mannheim", "family": "mannheim/taser", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2947, "dossier_id": "mcgill/medal/electra-medal", "name": "ELECTRA-MeDAL", "maker": "mcgill", "family": "mcgill/medal", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2948, "dossier_id": "mcmaster/medte/medte", "name": "MedTE", "maker": "mcmaster", "family": "mcmaster/medte", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2949, "dossier_id": "mila/guardrail/suicide-prevention-output-guardrail", "name": "Mila Suicide Prevention Output Guardrail", "maker": "mila", "family": "mila/guardrail", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2950, "dossier_id": "nagoya/ruri/ruri-base", "name": "Ruri base", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2951, "dossier_id": "nagoya/ruri/ruri-base-v2", "name": "Ruri base v2", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2952, "dossier_id": "nagoya/ruri/ruri-large", "name": "Ruri large", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2953, "dossier_id": "nagoya/ruri/ruri-large-v2", "name": "Ruri large v2", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2954, "dossier_id": "nagoya/ruri/ruri-pt-base", "name": "Ruri-PT base", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2955, "dossier_id": "nagoya/ruri/ruri-pt-base-v2", "name": "Ruri-PT base v2", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2956, "dossier_id": "nagoya/ruri/ruri-pt-large", "name": "Ruri-PT large", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2957, "dossier_id": "nagoya/ruri/ruri-pt-large-v2", "name": "Ruri-PT large v2", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2958, "dossier_id": "nagoya/ruri/ruri-pt-small", "name": "Ruri-PT small", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2959, "dossier_id": "nagoya/ruri/ruri-pt-small-v2", "name": "Ruri-PT small v2", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2960, "dossier_id": "nagoya/ruri/ruri-small", "name": "Ruri small", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2961, "dossier_id": "nagoya/ruri/ruri-small-v2", "name": "Ruri small v2", "maker": "nagoya", "family": "nagoya/ruri", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2962, "dossier_id": "nagoya/ruri-reranker/ruri-reranker-small", "name": "Ruri reranker small", "maker": "nagoya", "family": "nagoya/ruri-reranker", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2963, "dossier_id": "nagoya/simcse/sup-simcse-ja-base", "name": "Japanese supervised SimCSE base", "maker": "nagoya", "family": "nagoya/simcse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence embeddings. Supervised checkpoints train on JSNLI. Unsupervised checkpoints train on Wiki40B. The Ruri paper later treats these as baselines.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2964, "dossier_id": "nagoya/simcse/sup-simcse-ja-large", "name": "Japanese supervised SimCSE large", "maker": "nagoya", "family": "nagoya/simcse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence embeddings. Supervised checkpoints train on JSNLI. Unsupervised checkpoints train on Wiki40B. The Ruri paper later treats these as baselines.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2965, "dossier_id": "nagoya/simcse/unsup-simcse-ja-base", "name": "Japanese unsupervised SimCSE base", "maker": "nagoya", "family": "nagoya/simcse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence embeddings. Supervised checkpoints train on JSNLI. Unsupervised checkpoints train on Wiki40B. The Ruri paper later treats these as baselines.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2966, "dossier_id": "nagoya/simcse/unsup-simcse-ja-large", "name": "Japanese unsupervised SimCSE large", "maker": "nagoya", "family": "nagoya/simcse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence embeddings. Supervised checkpoints train on JSNLI. Unsupervised checkpoints train on Wiki40B. The Ruri paper later treats these as baselines.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2967, "dossier_id": "neu/techgpt/techgpt-2-0-alpaca", "name": "TechGPT-2.0-Alpaca", "maker": "neu", "family": "neu/techgpt", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2968, "dossier_id": "neu/techgpt/techgpt-2-0-atom", "name": "TechGPT-2.0-Atom", "maker": "neu", "family": "neu/techgpt", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2969, "dossier_id": "njust/fs-absa/laptop-t5-base", "name": "Laptop-T5-Base", "maker": "njust", "family": "njust/fs-absa", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2970, "dossier_id": "njust/fs-absa/restaurant-t5-base", "name": "Restaurant-T5-Base", "maker": "njust", "family": "njust/fs-absa", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2971, "dossier_id": "nomic/embed-text/nomic-embed-text-v2-moe", "name": "Nomic Embed Text v2 MoE", "maker": "nomic", "family": "nomic/embed-text", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2972, "dossier_id": "northwestern/dnabert/dnabert", "name": "DNABERT", "maker": "northwestern", "family": "northwestern/dnabert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2973, "dossier_id": "ntu-taiwan/bert-zhtw/bert-base-zhtw", "name": "bert-base-zhtw", "maker": "ntu-taiwan", "family": "ntu-taiwan/bert-zhtw", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2974, "dossier_id": "osaka/oubiobert/oubiobert-base-uncased", "name": "ouBioBERT-Base, Uncased", "maker": "osaka", "family": "osaka/oubiobert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2975, "dossier_id": "oslo/norbert/norbert", "name": "NorBERT", "maker": "oslo", "family": "oslo/norbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2976, "dossier_id": "oslo/norbert/norbert2", "name": "NorBERT 2", "maker": "oslo", "family": "oslo/norbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2977, "dossier_id": "patras/greek-reddit-bert/greek-reddit-bert", "name": "Greek-Reddit-BERT", "maker": "patras", "family": "patras/greek-reddit-bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2978, "dossier_id": "penn/percival/percival-b", "name": "Percival-B", "maker": "penn", "family": "penn/percival", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released Percival-B. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2979, "dossier_id": "penn/percival/percival-s", "name": "Percival-S", "maker": "penn", "family": "penn/percival", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released Percival-S. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2980, "dossier_id": "penn/percival/percival-t", "name": "Percival-T", "maker": "penn", "family": "penn/percival", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released Percival-T. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2981, "dossier_id": "polyu/utterance-segmentation/cantonese-utterance-segmentation", "name": "Cantonese Utterance Segmentation", "maker": "polyu", "family": "polyu/utterance-segmentation", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2982, "dossier_id": "princeton/qurater/qurater-1-3b", "name": "QuRater-1.3B", "maker": "princeton", "family": "princeton/qurater", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2983, "dossier_id": "radboud/dragon/dragon-bert-base-domain-specific", "name": "DRAGON BERT base domain-specific", "maker": "radboud", "family": "radboud/dragon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2984, "dossier_id": "radboud/dragon/dragon-roberta-base-domain-specific", "name": "DRAGON RoBERTa base domain-specific", "maker": "radboud", "family": "radboud/dragon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2985, "dossier_id": "radboud/dragon/dragon-roberta-large-domain-specific", "name": "DRAGON RoBERTa large domain-specific", "maker": "radboud", "family": "radboud/dragon", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2986, "dossier_id": "ruc/yulan-classifier/code-classifier", "name": "Code classifier", "maker": "ruc", "family": "ruc/yulan-classifier", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2987, "dossier_id": "ruc/yulan-classifier/math-classifier", "name": "Math classifier", "maker": "ruc", "family": "ruc/yulan-classifier", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2988, "dossier_id": "ruc/yulan-classifier/reasoning-classifier", "name": "Reasoning classifier", "maker": "ruc", "family": "ruc/yulan-classifier", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2989, "dossier_id": "sber/frida/frida", "name": "FRIDA", "maker": "sber", "family": "sber/frida", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2990, "dossier_id": "sber/giga-embeddings/embeddings", "name": "Embeddings", "maker": "sber", "family": "sber/giga-embeddings", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2991, "dossier_id": "sber/giga-embeddings/embeddings-2", "name": "Embeddings-2", "maker": "sber", "family": "sber/giga-embeddings", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2992, "dossier_id": "scut/lilt/lilt-infoxlm-base", "name": "LiLT-InfoXLM-base", "maker": "scut", "family": "scut/lilt", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper uses this variant for multilingual structured document understanding. Downstream fine-tuning includes FUNSD and XFUND.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2993, "dossier_id": "scut/lilt/lilt-only-base", "name": "LiLT-only-base", "maker": "scut", "family": "scut/lilt", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2994, "dossier_id": "scut/lilt/lilt-roberta-en-base", "name": "LiLT-RoBERTa-EN-base", "maker": "scut", "family": "scut/lilt", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2995, "dossier_id": "sk-telecom/kobert/kobert-base-v1", "name": "KoBERT", "maker": "sk-telecom", "family": "sk-telecom/kobert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "KoBERT is a kobert model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2996, "dossier_id": "smu/cottention/cottention-bert", "name": "Cottention BERT", "maker": "smu", "family": "smu/cottention", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2997, "dossier_id": "smu/cottention/cottention-gpt-1-2b", "name": "Cottention GPT 1.2B", "maker": "smu", "family": "smu/cottention", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2998, "dossier_id": "snowflake/arctic-embed/arctic-embed-l", "name": "Arctic Embed L", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 2999, "dossier_id": "snowflake/arctic-embed/arctic-embed-m", "name": "Arctic Embed M", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3000, "dossier_id": "snowflake/arctic-embed/arctic-embed-m-v1-5", "name": "Arctic Embed M v1.5", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 512, "prompt_price_per_m": 0.03, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3001, "dossier_id": "snowflake/arctic-embed/arctic-embed-s", "name": "Arctic Embed S", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3002, "dossier_id": "snowflake/arctic-embed/arctic-embed-xs", "name": "Arctic Embed XS", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3003, "dossier_id": "snu/kr-bert/kr-bert", "name": "KR-BERT", "maker": "snu", "family": "snu/kr-bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3004, "dossier_id": "snu/kr-bert/kr-medium", "name": "KR-BERT-MEDIUM", "maker": "snu", "family": "snu/kr-bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3005, "dossier_id": "snu/kr-electra/kr-electra-discriminator", "name": "KR-ELECTRA discriminator", "maker": "snu", "family": "snu/kr-electra", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3006, "dossier_id": "snu/kr-electra/kr-electra-generator", "name": "KR-ELECTRA generator", "maker": "snu", "family": "snu/kr-electra", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3007, "dossier_id": "snu/kr-finbert/kr-finbert", "name": "KR-FinBert", "maker": "snu", "family": "snu/kr-finbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3008, "dossier_id": "snu/kr-finbert/kr-finbert-sc", "name": "KR-FinBert-SC", "maker": "snu", "family": "snu/kr-finbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3009, "dossier_id": "snu/kr-sbert/kr-sbert-v40k-kluenli-augsts", "name": "KR-SBERT-V40K-klueNLI-augSTS", "maker": "snu", "family": "snu/kr-sbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3010, "dossier_id": "stanford/backpack/backpack-1-4b", "name": "Backpack 1.4B", "maker": "stanford", "family": "stanford/backpack", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3011, "dossier_id": "stanford/backpack/backpack-gpt2", "name": "Backpack-GPT2", "maker": "stanford", "family": "stanford/backpack", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says the model is for studying interpretable methods in NLP. It is not fit for production.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3012, "dossier_id": "stockholm/sweclin/sweclin-bert", "name": "SweClin-BERT", "maker": "stockholm", "family": "stockholm/sweclin", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3013, "dossier_id": "stockholm/sweclin/swedeclin-bert", "name": "SweDeClin-BERT", "maker": "stockholm", "family": "stockholm/sweclin", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3014, "dossier_id": "stockmark/bart/bart-base-japanese-news", "name": "BART base Japanese news", "maker": "stockmark", "family": "stockmark/bart", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3015, "dossier_id": "szu/mg-motionllm/mg-motionllm-base", "name": "MG-MotionLLM GSPretrained-base", "maker": "szu", "family": "szu/mg-motionllm", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3016, "dossier_id": "szu/mg-motionllm/mg-motionllm-large", "name": "MG-MotionLLM GSPretrained-large", "maker": "szu", "family": "szu/mg-motionllm", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3017, "dossier_id": "szu/mg-motionllm/mg-motionllm-small", "name": "MG-MotionLLM GSPretrained-small", "maker": "szu", "family": "szu/mg-motionllm", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3018, "dossier_id": "tartu/estbert/estbert", "name": "EstBERT", "maker": "tartu", "family": "tartu/estbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3019, "dossier_id": "tartu/estbert/estbert-ner", "name": "EstBERT NER", "maker": "tartu", "family": "tartu/estbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3020, "dossier_id": "tau/spider/spider", "name": "Spider", "maker": "tau", "family": "tau/spider", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3021, "dossier_id": "tau/splinter/splinter-base", "name": "Splinter-base", "maker": "tau", "family": "tau/splinter", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3022, "dossier_id": "tau/splinter/splinter-large", "name": "Splinter-large", "maker": "tau", "family": "tau/splinter", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3023, "dossier_id": "tohoku/bert/bert-base-japanese", "name": "BERT base Japanese", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3024, "dossier_id": "tohoku/bert/bert-base-japanese-char", "name": "BERT base Japanese character", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3025, "dossier_id": "tohoku/bert/bert-base-japanese-char-v2", "name": "BERT base Japanese character v2", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3026, "dossier_id": "tohoku/bert/bert-base-japanese-char-v3", "name": "BERT base Japanese character v3", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3027, "dossier_id": "tohoku/bert/bert-base-japanese-char-whole-word-masking", "name": "BERT base Japanese character whole-word masking", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3028, "dossier_id": "tohoku/bert/bert-base-japanese-v2", "name": "BERT base Japanese v2", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3029, "dossier_id": "tohoku/bert/bert-base-japanese-v3", "name": "BERT base Japanese v3", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3030, "dossier_id": "tohoku/bert/bert-base-japanese-whole-word-masking", "name": "BERT base Japanese whole-word masking", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3031, "dossier_id": "tohoku/bert/bert-large-japanese", "name": "BERT large Japanese", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3032, "dossier_id": "tohoku/bert/bert-large-japanese-char", "name": "BERT large Japanese character", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3033, "dossier_id": "tohoku/bert/bert-large-japanese-char-v2", "name": "BERT large Japanese character v2", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3034, "dossier_id": "tohoku/bert/bert-large-japanese-v2", "name": "BERT large Japanese v2", "maker": "tohoku", "family": "tohoku/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3035, "dossier_id": "tsinghua/embedding/minicpm-embedding", "name": "MiniCPM-Embedding", "maker": "tsinghua", "family": "tsinghua/embedding", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings from the MiniCPM-2B checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3036, "dossier_id": "tu-darmstadt/msmarco/msmarco-bert-base-dot-v5", "name": "msmarco-bert-base-dot-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3037, "dossier_id": "tu-darmstadt/msmarco/msmarco-distilbert-base-tas-b", "name": "msmarco-distilbert-base-tas-b", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3038, "dossier_id": "tu-darmstadt/msmarco/msmarco-distilbert-dot-v5", "name": "msmarco-distilbert-dot-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3039, "dossier_id": "tu-darmstadt/multi-qa/multi-qa-distilbert-cos-v1", "name": "multi-qa-distilbert-cos-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/multi-qa", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3040, "dossier_id": "tu-darmstadt/multi-qa/multi-qa-distilbert-dot-v1", "name": "multi-qa-distilbert-dot-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/multi-qa", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3041, "dossier_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", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3042, "dossier_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", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3043, "dossier_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", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3044, "dossier_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", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3045, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-mpnet-base-v2", "name": "paraphrase-mpnet-base-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3046, "dossier_id": "tu-darmstadt/sbert/all-distilroberta-v1", "name": "all-distilroberta-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3047, "dossier_id": "tu-darmstadt/sbert/all-mpnet-base-v1", "name": "all-mpnet-base-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3048, "dossier_id": "turku/eccobert/eccobert-base-cased", "name": "ECCO-BERT base cased", "maker": "turku", "family": "turku/eccobert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3049, "dossier_id": "turku/finbert/finbert-base-cased", "name": "FinBERT base cased", "maker": "turku", "family": "turku/finbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3050, "dossier_id": "turku/finbert/finbert-base-uncased", "name": "FinBERT base uncased", "maker": "turku", "family": "turku/finbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3051, "dossier_id": "turku/finbert/finbert-large-cased", "name": "FinBERT large cased", "maker": "turku", "family": "turku/finbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3052, "dossier_id": "turku/finbert/finbert-large-toxicity", "name": "FinBERT large toxicity", "maker": "turku", "family": "turku/finbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3053, "dossier_id": "turku/finbert/megatron-finbert-1-3b", "name": "Megatron-FinBERT 1.3B", "maker": "turku", "family": "turku/finbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3054, "dossier_id": "turku/register/web-register-en", "name": "Web register classifier, English", "maker": "turku", "family": "turku/register", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3055, "dossier_id": "turku/register/web-register-multilingual", "name": "Web register classifier, multilingual", "maker": "turku", "family": "turku/register", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3056, "dossier_id": "turku/webqa/xlmr-qa-extraction-en", "name": "XLM-R QA extractor, English", "maker": "turku", "family": "turku/webqa", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3057, "dossier_id": "turku/webqa/xlmr-qa-extraction-fi", "name": "XLM-R QA extractor, Finnish", "maker": "turku", "family": "turku/webqa", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3058, "dossier_id": "turku/webqa/xlmr-qa-register", "name": "XLM-R QA register classifier", "maker": "turku", "family": "turku/webqa", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3059, "dossier_id": "turku/wikibert/wikibert", "name": "WikiBERT", "maker": "turku", "family": "turku/wikibert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3060, "dossier_id": "u-tokyo/bert/bert-base-japanese-fin", "name": "BERT-base Japanese finance", "maker": "u-tokyo", "family": "u-tokyo/bert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3061, "dossier_id": "u-tokyo/deberta/deberta-v2-base-japanese", "name": "DeBERTa V2 base Japanese", "maker": "u-tokyo", "family": "u-tokyo/deberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3062, "dossier_id": "u-tokyo/electra/electra-base-japanese", "name": "ELECTRA-base Japanese", "maker": "u-tokyo", "family": "u-tokyo/electra", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3063, "dossier_id": "u-tokyo/electra/electra-small-japanese", "name": "ELECTRA-small Japanese", "maker": "u-tokyo", "family": "u-tokyo/electra", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3064, "dossier_id": "u-tokyo/electra/electra-small-japanese-fin", "name": "ELECTRA-small Japanese finance", "maker": "u-tokyo", "family": "u-tokyo/electra", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3065, "dossier_id": "u-tokyo/electra/electra-small-paper-japanese", "name": "ELECTRA-small-paper Japanese", "maker": "u-tokyo", "family": "u-tokyo/electra", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3066, "dossier_id": "u-tokyo/electra/electra-small-paper-japanese-fin", "name": "ELECTRA-small-paper Japanese finance", "maker": "u-tokyo", "family": "u-tokyo/electra", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3067, "dossier_id": "ubc/arat5/arat5-base", "name": "AraT5-base", "maker": "ubc", "family": "ubc/arat5", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3068, "dossier_id": "ubc/arat5/arat5-msa-base", "name": "AraT5-msa-base", "maker": "ubc", "family": "ubc/arat5", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3069, "dossier_id": "ubc/arat5/arat5-msa-small", "name": "AraT5-msa-small", "maker": "ubc", "family": "ubc/arat5", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3070, "dossier_id": "ubc/arat5/arat5-tweet-base", "name": "AraT5-tweet-base", "maker": "ubc", "family": "ubc/arat5", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3071, "dossier_id": "ubc/arat5/arat5-tweet-small", "name": "AraT5-tweet-small", "maker": "ubc", "family": "ubc/arat5", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3072, "dossier_id": "ubc/arbert/arbert", "name": "ARBERT", "maker": "ubc", "family": "ubc/arbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3073, "dossier_id": "ubc/arbert/arbertv2", "name": "ARBERTv2", "maker": "ubc", "family": "ubc/arbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3074, "dossier_id": "ubc/arbert/marbert", "name": "MARBERT", "maker": "ubc", "family": "ubc/arbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3075, "dossier_id": "ubc/arbert/marbertv2", "name": "MARBERTv2", "maker": "ubc", "family": "ubc/arbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3076, "dossier_id": "ubc/serengeti/serengeti-e110", "name": "SERENGETI-E110", "maker": "ubc", "family": "ubc/serengeti", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3077, "dossier_id": "ubc/serengeti/serengeti-e250", "name": "SERENGETI-E250", "maker": "ubc", "family": "ubc/serengeti", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3078, "dossier_id": "ucas/jtcse/jtcse-bert", "name": "JTCSE-BERT", "maker": "ucas", "family": "ucas/jtcse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3079, "dossier_id": "ucas/jtcse/jtcse-bert-d", "name": "JTCSE-BERT-D", "maker": "ucas", "family": "ucas/jtcse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3080, "dossier_id": "ucas/jtcse/jtcse-bert-ucd", "name": "JTCSE-BERT-UCD", "maker": "ucas", "family": "ucas/jtcse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3081, "dossier_id": "ucas/tncse/tncse-bert", "name": "TNCSE-BERT", "maker": "ucas", "family": "ucas/tncse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3082, "dossier_id": "ucas/tncse/tncse-bert-d", "name": "TNCSE-BERT-D", "maker": "ucas", "family": "ucas/tncse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3083, "dossier_id": "ucas/tncse/tncse-bert-ucd", "name": "TNCSE-BERT-UCD", "maker": "ucas", "family": "ucas/tncse", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3084, "dossier_id": "ucla/newsbert/newsbert", "name": "NewsBERT", "maker": "ucla", "family": "ucla/newsbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the newsbert family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3085, "dossier_id": "ucla/plbart/plbart-base", "name": "PLBART base", "maker": "ucla", "family": "ucla/plbart", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the plbart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3086, "dossier_id": "ucla/plbart/plbart-large", "name": "PLBART large", "maker": "ucla", "family": "ucla/plbart", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the plbart family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3087, "dossier_id": "ucla/visualbert/visualbert", "name": "VisualBERT", "maker": "ucla", "family": "ucla/visualbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the visualbert family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3088, "dossier_id": "uclouvain/brabant/brabant-xvii", "name": "Brabant-XVII from scratch", "maker": "uclouvain", "family": "uclouvain/brabant", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3089, "dossier_id": "ucph/citebert/citebert", "name": "CiteBERT", "maker": "ucph", "family": "ucph/citebert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3090, "dossier_id": "ucph/citebert/citebert-cite-only", "name": "CiteBERT cite-only", "maker": "ucph", "family": "ucph/citebert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3091, "dossier_id": "ucph/danish-legal/danish-legal-bert-base", "name": "Danish Legal BERT base", "maker": "ucph", "family": "ucph/danish-legal", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3092, "dossier_id": "ucph/danish-legal/danish-legal-lm-base", "name": "Danish Legal LM base", "maker": "ucph", "family": "ucph/danish-legal", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3093, "dossier_id": "udel/arabict5/arabict5-17gb-base", "name": "ArabicT5-17GB-base", "maker": "udel", "family": "udel/arabict5", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3094, "dossier_id": "udel/arabict5/arabict5-17gb-large", "name": "ArabicT5-17GB-large", "maker": "udel", "family": "udel/arabict5", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3095, "dossier_id": "udel/arabict5/arabict5-17gb-small", "name": "ArabicT5-17GB-small", "maker": "udel", "family": "udel/arabict5", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3096, "dossier_id": "udel/arabictransformer/arabictransformer-base", "name": "ArabicTransformer-base", "maker": "udel", "family": "udel/arabictransformer", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3097, "dossier_id": "udel/arabictransformer/arabictransformer-large", "name": "ArabicTransformer-large", "maker": "udel", "family": "udel/arabictransformer", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3098, "dossier_id": "udel/arabictransformer/arabictransformer-small", "name": "ArabicTransformer-small", "maker": "udel", "family": "udel/arabictransformer", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3099, "dossier_id": "udel/biom/biom-albert-xxlarge", "name": "BioM-ALBERT-xxlarge", "maker": "udel", "family": "udel/biom", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3100, "dossier_id": "udel/biom/biom-albert-xxlarge-pmc", "name": "BioM-ALBERT-xxlarge-PMC", "maker": "udel", "family": "udel/biom", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3101, "dossier_id": "udel/biom/biom-bert-pubmed-pmc-large", "name": "BioM-BERT-PubMed-PMC-Large", "maker": "udel", "family": "udel/biom", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3102, "dossier_id": "udel/biom/biom-electra-base", "name": "BioM-ELECTRA-Base", "maker": "udel", "family": "udel/biom", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3103, "dossier_id": "udel/biom/biom-electra-large", "name": "BioM-ELECTRA-Large", "maker": "udel", "family": "udel/biom", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3104, "dossier_id": "ufl/gatortron/gatortron-base", "name": "GatorTron-Base", "maker": "ufl", "family": "ufl/gatortron", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3105, "dossier_id": "ufl/gatortron/gatortron-large", "name": "GatorTron-Large", "maker": "ufl", "family": "ufl/gatortron", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3106, "dossier_id": "ufl/gatortron/gatortron-medium", "name": "GatorTron-Medium", "maker": "ufl", "family": "ufl/gatortron", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3107, "dossier_id": "ufl/gatortron/gatortron-s", "name": "GatorTronS", "maker": "ufl", "family": "ufl/gatortron", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3108, "dossier_id": "umn/geolm/geolm-base-cased", "name": "GeoLM-base-cased", "maker": "umn", "family": "umn/geolm", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3109, "dossier_id": "umn/geolm/geolm-base-toponym-recognition", "name": "GeoLM toponym recognition", "maker": "umn", "family": "umn/geolm", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3110, "dossier_id": "umn/scholawrite/scholawrite-bert-classifier", "name": "ScholaWrite BERT classifier", "maker": "umn", "family": "umn/scholawrite", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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 ``.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3111, "dossier_id": "unc/lxmert/lxmert", "name": "LXMERT", "maker": "unc", "family": "unc/lxmert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3112, "dossier_id": "unsw/energybert/energybert", "name": "EnergyBERT", "maker": "unsw", "family": "unsw/energybert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3113, "dossier_id": "upm/star/star", "name": "STAR", "maker": "upm", "family": "upm/star", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3114, "dossier_id": "uq/starbucks/starbucks-sts", "name": "Starbucks STS", "maker": "uq", "family": "uq/starbucks", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3115, "dossier_id": "ustb/steelbert/steelbert", "name": "SteelBERT", "maker": "ustb", "family": "ustb/steelbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3116, "dossier_id": "uzh/swissbert/swissbert", "name": "SwissBERT", "maker": "uzh", "family": "uzh/swissbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3117, "dossier_id": "uzh/swissbert/swissbert-xlm-vocab", "name": "SwissBERT XLM vocabulary", "maker": "uzh", "family": "uzh/swissbert", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3118, "dossier_id": "vu-amsterdam/medroberta/medroberta-nl", "name": "MedRoBERTa.nl", "maker": "vu-amsterdam", "family": "vu-amsterdam/medroberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3119, "dossier_id": "waseda/roberta/roberta-large-japanese-seq512", "name": "Waseda RoBERTa large Japanese (seq512)", "maker": "waseda", "family": "waseda/roberta", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3120, "dossier_id": "yale/autoacu/a2cu-classifier", "name": "A2CU Classifier", "maker": "yale", "family": "yale/autoacu", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3121, "dossier_id": "yale/autoacu/a3cu", "name": "A3CU", "maker": "yale", "family": "yale/autoacu", "context_length": 512, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3122, "dossier_id": "naver/embedding/clir-emb-dolphin", "name": "clir-emb-dolphin", "maker": "naver", "family": "naver/embedding", "context_length": 500, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3123, "dossier_id": "naver/embedding/clir-sts-dolphin", "name": "clir-sts-dolphin", "maker": "naver", "family": "naver/embedding", "context_length": 500, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The guide lists it for sentence similarity. It does not publish a parameter count or a weight license.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3124, "dossier_id": "basque/medical-mt5/medical-mt5-xl", "name": "Medical mT5 XL", "maker": "basque", "family": "basque/medical-mt5", "context_length": 480, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3125, "dossier_id": "baidu/embedding/embedding-v1", "name": "Embedding-V1", "maker": "baidu", "family": "baidu/embedding", "context_length": 384, "prompt_price_per_m": 0.0005, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Short-text embeddings for search and knowledge bases.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3126, "dossier_id": "tu-darmstadt/sbert/all-mpnet-base-v2", "name": "all-mpnet-base-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "context_length": 384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3127, "dossier_id": "turku/finbert/finbert-base-squad2", "name": "FinBERT base SQuAD2", "maker": "turku", "family": "turku/finbert", "context_length": 384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3128, "dossier_id": "ucph/spiced/spiced", "name": "SPICED", "maker": "ucph", "family": "ucph/spiced", "context_length": 384, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3129, "dossier_id": "amazon/titan/titan-multimodal-embeddings-g1", "name": "Amazon Titan Multimodal Embeddings G1", "maker": "amazon", "family": "amazon/titan", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3130, "dossier_id": "kuaishou/kolors/kolors", "name": "Kolors", "maker": "kuaishou", "family": "kuaishou/kolors", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The maker says it is for photorealistic text-to-image generation, complex prompts, and Chinese and English text rendering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3131, "dossier_id": "msu/bet/bet", "name": "BET", "maker": "msu", "family": "msu/bet", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3132, "dossier_id": "nagoya/shioriha/shioriha-large-pt", "name": "Shioriha large PT", "maker": "nagoya", "family": "nagoya/shioriha", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3133, "dossier_id": "nd/autorepair/autorepair", "name": "Autorepair", "maker": "nd", "family": "nd/autorepair", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3134, "dossier_id": "nd/jam/jam", "name": "Jam", "maker": "nd", "family": "nd/jam", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3135, "dossier_id": "nd/jam/jam-cgpt", "name": "Jam-CGPT", "maker": "nd", "family": "nd/jam", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3136, "dossier_id": "nd/jam/jam-so", "name": "Jam-SO", "maker": "nd", "family": "nd/jam", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3137, "dossier_id": "nd/jam/jam-sojm", "name": "Jam-SOJM", "maker": "nd", "family": "nd/jam", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3138, "dossier_id": "toronto/open-pmc/open-pmc-18m-clip", "name": "Open-PMC-18M CLIP", "maker": "toronto", "family": "toronto/open-pmc", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3139, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-albert-small-v2", "name": "paraphrase-albert-small-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3140, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-distilroberta-base-v2", "name": "paraphrase-distilroberta-base-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3141, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-minilm-l12-v2", "name": "paraphrase-MiniLM-L12-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3142, "dossier_id": "tu-darmstadt/sbert/all-minilm-l12-v1", "name": "all-MiniLM-L12-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3143, "dossier_id": "tu-darmstadt/sbert/all-minilm-l12-v2", "name": "all-MiniLM-L12-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3144, "dossier_id": "tu-darmstadt/sbert/all-minilm-l6-v2", "name": "all-MiniLM-L6-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3145, "dossier_id": "tu-darmstadt/sbert/all-roberta-large-v1", "name": "all-roberta-large-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3146, "dossier_id": "upm/bertuit/bertuit-base", "name": "BERTuit-base", "maker": "upm", "family": "upm/bertuit", "context_length": 256, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3147, "dossier_id": "scarolina/bertos/bertos", "name": "BERTOS", "maker": "scarolina", "family": "scarolina/bertos", "context_length": 200, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3148, "dossier_id": "uq/diffretriever/diffretriever-llada-8b", "name": "DiffRetriever LLaDA 8B", "maker": "uq", "family": "uq/diffretriever", "context_length": 156, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3149, "dossier_id": "aisingapore/sea-lion-bert/sealion-bert-base", "name": "SEA-LION-BERT-Base", "maker": "aisingapore", "family": "aisingapore/sea-lion-bert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fill-mask and encoder features for the 11 SEA-LION v1 languages. Sequence length on the card is 128.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3150, "dossier_id": "antwerp/mfaq/mfaq", "name": "MFAQ", "maker": "antwerp", "family": "antwerp/mfaq", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3151, "dossier_id": "bgu/tafberta/tafberta", "name": "TafBERTa", "maker": "bgu", "family": "bgu/tafberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3152, "dossier_id": "byu/mekongphon/khmer-ipa", "name": "Khmer IPA", "maker": "byu", "family": "byu/mekongphon", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3153, "dossier_id": "byu/mekongphon/lao-ipa-nontonal", "name": "Lao IPA (Non-tonal)", "maker": "byu", "family": "byu/mekongphon", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3154, "dossier_id": "byu/mekongphon/lao-ipa-tonal", "name": "Lao IPA (Tonal)", "maker": "byu", "family": "byu/mekongphon", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3155, "dossier_id": "databricks/mosaic-bert/mosaic-bert-base", "name": "MosaicBERT-Base", "maker": "databricks", "family": "databricks/mosaic-bert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3156, "dossier_id": "fiu/moldeberta/moldeberta-base-10m-contrastive-mlc", "name": "MolDeBERTa-base-10M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3157, "dossier_id": "fiu/moldeberta/moldeberta-base-10m-contrastive-mtr", "name": "MolDeBERTa-base-10M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3158, "dossier_id": "fiu/moldeberta/moldeberta-base-10m-mlc", "name": "MolDeBERTa-base-10M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3159, "dossier_id": "fiu/moldeberta/moldeberta-base-10m-mlm", "name": "MolDeBERTa-base-10M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3160, "dossier_id": "fiu/moldeberta/moldeberta-base-10m-mtr", "name": "MolDeBERTa-base-10M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3161, "dossier_id": "fiu/moldeberta/moldeberta-base-123m-contrastive-mlc", "name": "MolDeBERTa-base-123M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3162, "dossier_id": "fiu/moldeberta/moldeberta-base-123m-contrastive-mtr", "name": "MolDeBERTa-base-123M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3163, "dossier_id": "fiu/moldeberta/moldeberta-base-123m-mlc", "name": "MolDeBERTa-base-123M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3164, "dossier_id": "fiu/moldeberta/moldeberta-base-123m-mlm", "name": "MolDeBERTa-base-123M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3165, "dossier_id": "fiu/moldeberta/moldeberta-base-123m-mtr", "name": "MolDeBERTa-base-123M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3166, "dossier_id": "fiu/moldeberta/moldeberta-small-10m-contrastive-mlc", "name": "MolDeBERTa-small-10M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3167, "dossier_id": "fiu/moldeberta/moldeberta-small-10m-contrastive-mtr", "name": "MolDeBERTa-small-10M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3168, "dossier_id": "fiu/moldeberta/moldeberta-small-10m-mlc", "name": "MolDeBERTa-small-10M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3169, "dossier_id": "fiu/moldeberta/moldeberta-small-10m-mlm", "name": "MolDeBERTa-small-10M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3170, "dossier_id": "fiu/moldeberta/moldeberta-small-10m-mtr", "name": "MolDeBERTa-small-10M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3171, "dossier_id": "fiu/moldeberta/moldeberta-small-123m-contrastive-mlc", "name": "MolDeBERTa-small-123M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3172, "dossier_id": "fiu/moldeberta/moldeberta-small-123m-contrastive-mtr", "name": "MolDeBERTa-small-123M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3173, "dossier_id": "fiu/moldeberta/moldeberta-small-123m-mlc", "name": "MolDeBERTa-small-123M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3174, "dossier_id": "fiu/moldeberta/moldeberta-small-123m-mlm", "name": "MolDeBERTa-small-123M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3175, "dossier_id": "fiu/moldeberta/moldeberta-small-123m-mtr", "name": "MolDeBERTa-small-123M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3176, "dossier_id": "fiu/moldeberta/moldeberta-tiny-10m-contrastive-mlc", "name": "MolDeBERTa-tiny-10M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3177, "dossier_id": "fiu/moldeberta/moldeberta-tiny-10m-contrastive-mtr", "name": "MolDeBERTa-tiny-10M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3178, "dossier_id": "fiu/moldeberta/moldeberta-tiny-10m-mlc", "name": "MolDeBERTa-tiny-10M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3179, "dossier_id": "fiu/moldeberta/moldeberta-tiny-10m-mlm", "name": "MolDeBERTa-tiny-10M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3180, "dossier_id": "fiu/moldeberta/moldeberta-tiny-10m-mtr", "name": "MolDeBERTa-tiny-10M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3181, "dossier_id": "fiu/moldeberta/moldeberta-tiny-123m-contrastive-mlc", "name": "MolDeBERTa-tiny-123M-contrastive_mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3182, "dossier_id": "fiu/moldeberta/moldeberta-tiny-123m-contrastive-mtr", "name": "MolDeBERTa-tiny-123M-contrastive_mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3183, "dossier_id": "fiu/moldeberta/moldeberta-tiny-123m-mlc", "name": "MolDeBERTa-tiny-123M-mlc", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3184, "dossier_id": "fiu/moldeberta/moldeberta-tiny-123m-mlm", "name": "MolDeBERTa-tiny-123M-mlm", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3185, "dossier_id": "fiu/moldeberta/moldeberta-tiny-123m-mtr", "name": "MolDeBERTa-tiny-123M-mtr", "maker": "fiu", "family": "fiu/moldeberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3186, "dossier_id": "jhu/bernice/bernice", "name": "Bernice", "maker": "jhu", "family": "jhu/bernice", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3187, "dossier_id": "kyoto/bert/japanese-bert-base", "name": "Japanese BERT base", "maker": "kyoto", "family": "kyoto/bert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3188, "dossier_id": "kyoto/bert/japanese-bert-base-wwm", "name": "Japanese BERT base WWM", "maker": "kyoto", "family": "kyoto/bert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3189, "dossier_id": "kyoto/bert/japanese-bert-large-wwm", "name": "Japanese BERT large WWM", "maker": "kyoto", "family": "kyoto/bert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3190, "dossier_id": "lisbon/serafim/serafim-100m", "name": "Serafim 100M", "maker": "lisbon", "family": "lisbon/serafim", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3191, "dossier_id": "lisbon/serafim/serafim-100m-ir", "name": "Serafim 100M IR", "maker": "lisbon", "family": "lisbon/serafim", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3192, "dossier_id": "lisbon/serafim/serafim-335m", "name": "Serafim 335M", "maker": "lisbon", "family": "lisbon/serafim", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3193, "dossier_id": "lisbon/serafim/serafim-335m-ir", "name": "Serafim 335M IR", "maker": "lisbon", "family": "lisbon/serafim", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3194, "dossier_id": "lisbon/serafim/serafim-900m", "name": "Serafim 900M", "maker": "lisbon", "family": "lisbon/serafim", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3195, "dossier_id": "lisbon/serafim/serafim-900m-ir", "name": "Serafim 900M IR", "maker": "lisbon", "family": "lisbon/serafim", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3196, "dossier_id": "lyon/bebeshka/bebeshka", "name": "Bebeshka", "maker": "lyon", "family": "lyon/bebeshka", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3197, "dossier_id": "osaka/medbertjp/medbertjp-mecab-ipadic", "name": "medBERTjp (MeCab-IPAdic)", "maker": "osaka", "family": "osaka/medbertjp", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3198, "dossier_id": "osaka/medbertjp/medbertjp-mecab-ipadic-neologd-jmedic", "name": "medBERTjp (MeCab-IPAdic-NEologd-JMeDic)", "maker": "osaka", "family": "osaka/medbertjp", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3199, "dossier_id": "osaka/medbertjp/medbertjp-sentencepiece", "name": "medBERTjp (SentencePiece)", "maker": "osaka", "family": "osaka/medbertjp", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3200, "dossier_id": "osaka/medbertjp/medbertjp-unidic", "name": "medBERTjp (UniDic 2.3.0)", "maker": "osaka", "family": "osaka/medbertjp", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3201, "dossier_id": "saarland/da4mt/da4mt-adapted", "name": "DA4MT MTR domain-adapted checkpoints", "maker": "saarland", "family": "saarland/da4mt", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3202, "dossier_id": "saarland/da4mt/da4mt-mlm-30", "name": "DA4MT MLM 30%", "maker": "saarland", "family": "saarland/da4mt", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3203, "dossier_id": "saarland/da4mt/da4mt-mlm-60", "name": "DA4MT MLM 60%", "maker": "saarland", "family": "saarland/da4mt", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3204, "dossier_id": "saarland/da4mt/da4mt-mtr-30", "name": "DA4MT MTR 30%", "maker": "saarland", "family": "saarland/da4mt", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3205, "dossier_id": "saarland/da4mt/da4mt-mtr-60", "name": "DA4MT MTR 60%", "maker": "saarland", "family": "saarland/da4mt", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3206, "dossier_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", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3207, "dossier_id": "tu-darmstadt/distiluse/distiluse-base-multilingual-cased-v1", "name": "distiluse-base-multilingual-cased-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/distiluse", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3208, "dossier_id": "tu-darmstadt/distiluse/distiluse-base-multilingual-cased-v2", "name": "distiluse-base-multilingual-cased-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/distiluse", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3209, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-minilm-l3-v2", "name": "paraphrase-MiniLM-L3-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest paraphrase MiniLM in the SBERT pretrained table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3210, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-minilm-l6-v2", "name": "paraphrase-MiniLM-L6-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3211, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-multilingual-minilm-l12-v2", "name": "paraphrase-multilingual-MiniLM-L12-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3212, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-multilingual-mpnet-base-v2", "name": "paraphrase-multilingual-mpnet-base-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3213, "dossier_id": "tu-darmstadt/paraphrase/paraphrase-tinybert-l6-v2", "name": "paraphrase-TinyBERT-L6-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/paraphrase", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English paraphrase sentence embedding model from the SBERT pretrained table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3214, "dossier_id": "tu-darmstadt/sbert/all-minilm-l6-v1", "name": "all-MiniLM-L6-v1", "maker": "tu-darmstadt", "family": "tu-darmstadt/sbert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3215, "dossier_id": "turku/sbert/sbert-cased-finnish-paraphrase", "name": "Finnish Sentence-BERT cased", "maker": "turku", "family": "turku/sbert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3216, "dossier_id": "turku/sbert/sbert-uncased-finnish-paraphrase", "name": "Finnish Sentence-BERT uncased", "maker": "turku", "family": "turku/sbert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3217, "dossier_id": "u-tokyo/bert/bert-small-japanese", "name": "BERT-small Japanese", "maker": "u-tokyo", "family": "u-tokyo/bert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3218, "dossier_id": "u-tokyo/bert/bert-small-japanese-fin", "name": "BERT-small Japanese finance", "maker": "u-tokyo", "family": "u-tokyo/bert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3219, "dossier_id": "u-tokyo/deberta/deberta-v2-small-japanese", "name": "DeBERTa V2 small Japanese", "maker": "u-tokyo", "family": "u-tokyo/deberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3220, "dossier_id": "uclouvain/bertinchamps/bertinchamps-classic", "name": "BERTinchamps classic", "maker": "uclouvain", "family": "uclouvain/bertinchamps", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3221, "dossier_id": "uclouvain/bertinchamps/bertinchamps-rtbf", "name": "BERTinchamps RTBF", "maker": "uclouvain", "family": "uclouvain/bertinchamps", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3222, "dossier_id": "umd/crammed-bert/crammed-bert", "name": "Crammed BERT", "maker": "umd", "family": "umd/crammed-bert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3223, "dossier_id": "uzh/swissbert/swissbert-cs", "name": "SwissBERT-CS", "maker": "uzh", "family": "uzh/swissbert", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3224, "dossier_id": "warsaw/hyformer/hyformer-molecules-50m", "name": "Hyformer molecules 50M", "maker": "warsaw", "family": "warsaw/hyformer", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3225, "dossier_id": "warsaw/hyformer/hyformer-molecules-8m", "name": "Hyformer molecules 8M", "maker": "warsaw", "family": "warsaw/hyformer", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3226, "dossier_id": "waseda/roberta/roberta-base-japanese", "name": "Waseda RoBERTa base Japanese", "maker": "waseda", "family": "waseda/roberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3227, "dossier_id": "waseda/roberta/roberta-large-japanese", "name": "Waseda RoBERTa large Japanese", "maker": "waseda", "family": "waseda/roberta", "context_length": 128, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3228, "dossier_id": "harvard/conch/conch-z", "name": "CONCH-Z", "maker": "harvard", "family": "harvard/conch", "context_length": 127, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3229, "dossier_id": "bern/h3berta/h3berta", "name": "H3BERTa", "maker": "bern", "family": "bern/h3berta", "context_length": 100, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3230, "dossier_id": "linz/bio-xlstm/chem-xlstm-15m", "name": "Chem-xLSTM-15M", "maker": "linz", "family": "linz/bio-xlstm", "context_length": 100, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3231, "dossier_id": "apple/aimv2/aimv2-large-lit", "name": "AIMv2-large-lit", "maker": "apple", "family": "apple/aimv2", "context_length": 77, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image classification and image-text embeddings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3232, "dossier_id": "goettingen/clipseg/clipseg-rd16", "name": "CLIPSeg RD16", "maker": "goettingen", "family": "goettingen/clipseg", "context_length": 77, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3233, "dossier_id": "goettingen/clipseg/clipseg-rd64", "name": "CLIPSeg RD64", "maker": "goettingen", "family": "goettingen/clipseg", "context_length": 77, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3234, "dossier_id": "goettingen/clipseg/clipseg-rd64-refined", "name": "CLIPSeg RD64 refined", "maker": "goettingen", "family": "goettingen/clipseg", "context_length": 77, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3235, "dossier_id": "lmu/ldm/ldm-text2im-large-256", "name": "LDM Text-to-Image Large 256", "maker": "lmu", "family": "lmu/ldm", "context_length": 77, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3236, "dossier_id": "kstate/pepbert/pepbert-large", "name": "PepBERT-large", "maker": "kstate", "family": "kstate/pepbert", "context_length": 52, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3237, "dossier_id": "kstate/pepbert/pepbert-small", "name": "PepBERT-small", "maker": "kstate", "family": "kstate/pepbert", "context_length": 52, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3238, "dossier_id": "freiburg/entitynet/entitynet-clip", "name": "EntityNet CLIP", "maker": "freiburg", "family": "freiburg/entitynet", "context_length": 32, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3239, "dossier_id": "utrecht/olifant/olifant", "name": "Olifant", "maker": "utrecht", "family": "utrecht/olifant", "context_length": 4, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3240, "dossier_id": "utrecht/olifant/olifant-speculative", "name": "Olifant speculative instance bases", "maker": "utrecht", "family": "utrecht/olifant", "context_length": 4, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3241, "dossier_id": "01-ai/yi-large/yi-large-preview", "name": "Yi-Large-Preview", "maker": "01-ai", "family": "01-ai/yi-large", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper uses it as the prior-generation comparison point for Yi-Lightning. No separate product page was retrieved.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3242, "dossier_id": "01-ai/yi-lightning/yi-lightning-lite", "name": "Yi-Lightning-Lite", "maker": "01-ai", "family": "01-ai/yi-lightning", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3243, "dossier_id": "01-ai/yi-vl/yi-vl-34b", "name": "Yi-VL-34B", "maker": "01-ai", "family": "01-ai/yi-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3244, "dossier_id": "01-ai/yi-vl/yi-vl-6b", "name": "Yi-VL-6B", "maker": "01-ai", "family": "01-ai/yi-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same single-image bilingual visual question answering as Yi-VL-34B, at the 6B size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3245, "dossier_id": "aalborg/audiomae-plus-plus/audiomae-plus-plus-base", "name": "AudioMAE++-Base", "maker": "aalborg", "family": "aalborg/audiomae-plus-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3246, "dossier_id": "aalborg/audiomae-plus-plus/audiomae-plus-plus-large", "name": "AudioMAE++-Large", "maker": "aalborg", "family": "aalborg/audiomae-plus-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3247, "dossier_id": "aalborg/audiomae-plus-plus/audiomae-plus-plus-tiny", "name": "AudioMAE++-Tiny", "maker": "aalborg", "family": "aalborg/audiomae-plus-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A frozen general-purpose audio embedding. The README's extract script runs this Tiny config by default, together with Base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3248, "dossier_id": "aalborg/axlstm/axlstm-base", "name": "AxLSTM-Base", "maker": "aalborg", "family": "aalborg/axlstm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3249, "dossier_id": "aalborg/axlstm/axlstm-small", "name": "AxLSTM-Small", "maker": "aalborg", "family": "aalborg/axlstm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A frozen audio embedding for the same ten-task HEAR subset used across this lab's papers.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3250, "dossier_id": "aalborg/axlstm/axlstm-tiny", "name": "AxLSTM-Tiny", "maker": "aalborg", "family": "aalborg/axlstm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A frozen general-purpose audio embedding. The official repo exposes `axlstm_tiny_200_16x4` for HEAR feature extraction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3251, "dossier_id": "aalborg/ssam/ssam-base", "name": "SSAM-Base", "maker": "aalborg", "family": "aalborg/ssam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3252, "dossier_id": "aalborg/ssam/ssam-small", "name": "SSAM-Small", "maker": "aalborg", "family": "aalborg/ssam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3253, "dossier_id": "aalborg/ssam/ssam-tiny", "name": "SSAM-Tiny", "maker": "aalborg", "family": "aalborg/ssam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3254, "dossier_id": "aalto/bayesvlm/bayesvlm-base", "name": "BayesVLM-Base", "maker": "aalto", "family": "aalto/bayesvlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3255, "dossier_id": "aalto/bayesvlm/bayesvlm-large", "name": "BayesVLM-Large", "maker": "aalto", "family": "aalto/bayesvlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3256, "dossier_id": "aalto/combfinnish/combfinnish-aed-crdnn", "name": "CombFinnish AED-CRDNN", "maker": "aalto", "family": "aalto/combfinnish", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3257, "dossier_id": "aalto/hifi-glot/hifi-glot", "name": "HiFi-Glot", "maker": "aalto", "family": "aalto/hifi-glot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3258, "dossier_id": "aalto/multitask-wav2vec2/wav2vec2-large-multitask-finnish-l2", "name": "Finnish L2 multi-task wav2vec2", "maker": "aalto", "family": "aalto/multitask-wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3259, "dossier_id": "aalto/multitask-wav2vec2/wav2vec2-large-multitask-swedish-ssd", "name": "Swedish multi-task wav2vec2 SSD", "maker": "aalto", "family": "aalto/multitask-wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3260, "dossier_id": "aalto/mus3d/mus3d", "name": "MuS3D", "maker": "aalto", "family": "aalto/mus3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3261, "dossier_id": "aalto/smelp/smelp", "name": "smelp", "maker": "aalto", "family": "aalto/smelp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3262, "dossier_id": "aalto/wav2vec2-fi/wav2vec2-base-fi-150k", "name": "Finnish Wav2Vec2-Base", "maker": "aalto", "family": "aalto/wav2vec2-fi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3263, "dossier_id": "aalto/wav2vec2-fi/wav2vec2-base-fi-150k-finetuned", "name": "Finnish Wav2Vec2-Base ASR", "maker": "aalto", "family": "aalto/wav2vec2-fi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3264, "dossier_id": "aalto/wav2vec2-fi/wav2vec2-large-fi-150k", "name": "Finnish Wav2Vec2-Large", "maker": "aalto", "family": "aalto/wav2vec2-fi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3265, "dossier_id": "aalto/wav2vec2-fi/wav2vec2-large-fi-150k-finetuned", "name": "Finnish Wav2Vec2-Large ASR", "maker": "aalto", "family": "aalto/wav2vec2-fi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3266, "dossier_id": "aalto/wav2vec2-fi/wav2vec2-xlarge-fi-150k", "name": "Finnish Wav2Vec2-XLarge", "maker": "aalto", "family": "aalto/wav2vec2-fi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3267, "dossier_id": "aalto/wav2vec2-fi/wav2vec2-xlarge-fi-150k-finetuned", "name": "Finnish Wav2Vec2-XLarge ASR", "maker": "aalto", "family": "aalto/wav2vec2-fi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3268, "dossier_id": "aalto/wav2vec2-sami/wav2vec2-base-sami-22k", "name": "Northern S\u00e1mi Wav2Vec2-Base", "maker": "aalto", "family": "aalto/wav2vec2-sami", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3269, "dossier_id": "aalto/wav2vec2-sami/wav2vec2-base-sami-22k-finetuned", "name": "Northern S\u00e1mi Wav2Vec2-Base ASR", "maker": "aalto", "family": "aalto/wav2vec2-sami", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3270, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3271, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3272, "dossier_id": "aalto/wav2vec2-sami/wav2vec2-large-sami-22k", "name": "Northern S\u00e1mi Wav2Vec2-Large", "maker": "aalto", "family": "aalto/wav2vec2-sami", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3273, "dossier_id": "aalto/wav2vec2-sami/wav2vec2-large-sami-22k-finetuned", "name": "Northern S\u00e1mi Wav2Vec2-Large ASR", "maker": "aalto", "family": "aalto/wav2vec2-sami", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3274, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3275, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3276, "dossier_id": "aarhus/dacy/da-dacy-edge", "name": "DaCy edge", "maker": "aarhus", "family": "aarhus/dacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3277, "dossier_id": "aarhus/dacy/da-dacy-large-ner-fine-grained", "name": "DaCy large fine-grained NER", "maker": "aarhus", "family": "aarhus/dacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3278, "dossier_id": "aarhus/dacy/da-dacy-large-trf", "name": "DaCy large", "maker": "aarhus", "family": "aarhus/dacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3279, "dossier_id": "aarhus/dacy/da-dacy-medium-ner-fine-grained", "name": "DaCy medium fine-grained NER", "maker": "aarhus", "family": "aarhus/dacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3280, "dossier_id": "aarhus/dacy/da-dacy-medium-trf", "name": "DaCy medium", "maker": "aarhus", "family": "aarhus/dacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3281, "dossier_id": "aarhus/dacy/da-dacy-small-ner-fine-grained", "name": "DaCy small fine-grained NER", "maker": "aarhus", "family": "aarhus/dacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3282, "dossier_id": "aarhus/dacy/da-dacy-small-trf", "name": "DaCy small", "maker": "aarhus", "family": "aarhus/dacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3283, "dossier_id": "aarhus/dacy/da-dacy-tiny", "name": "DaCy tiny", "maker": "aarhus", "family": "aarhus/dacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3284, "dossier_id": "aarhus/odycy/grc-odycy-joint-sm", "name": "OdyCy joint small", "maker": "aarhus", "family": "aarhus/odycy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3285, "dossier_id": "aarhus/odycy/grc-odycy-joint-trf", "name": "OdyCy joint transformer", "maker": "aarhus", "family": "aarhus/odycy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3286, "dossier_id": "aberdeen/equicaps/equicaps", "name": "EquiCaps", "maker": "aberdeen", "family": "aberdeen/equicaps", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3287, "dossier_id": "aberystwyth/deepcanola/deepcanola", "name": "DeepCanola", "maker": "aberystwyth", "family": "aberystwyth/deepcanola", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3288, "dossier_id": "aberystwyth/deeppod/deeppod", "name": "DeepPod", "maker": "aberystwyth", "family": "aberystwyth/deeppod", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3289, "dossier_id": "aberystwyth/morphpod/morphpod", "name": "MorphPod", "maker": "aberystwyth", "family": "aberystwyth/morphpod", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3290, "dossier_id": "adept/act/act-1", "name": "ACT-1", "maker": "adept", "family": "adept/act", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3291, "dossier_id": "adept/act/act-2", "name": "ACT-2", "maker": "adept", "family": "adept/act", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3292, "dossier_id": "adept/fuyu/fuyu-heavy", "name": "Fuyu-Heavy", "maker": "adept", "family": "adept/fuyu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3293, "dossier_id": "adept/fuyu/fuyu-medium", "name": "Fuyu-Medium", "maker": "adept", "family": "adept/fuyu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3294, "dossier_id": "adobe/firefly-audio/firefly-music", "name": "Firefly Music Model", "maker": "adobe", "family": "adobe/firefly-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 20.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Music Model is a firefly-audio model. Status on 2026-10-15: active. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3295, "dossier_id": "adobe/firefly-audio/firefly-speech", "name": "Firefly Speech Model", "maker": "adobe", "family": "adobe/firefly-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Speech Model is a firefly-audio model. Status on 2026-10-15: active. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3296, "dossier_id": "adobe/firefly-composite/firefly-composite-md", "name": "Firefly Composite MD", "maker": "adobe", "family": "adobe/firefly-composite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Composite MD is a firefly-composite model. Status on 2026-10-15: retired. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3297, "dossier_id": "adobe/firefly-composite/firefly-composite-me", "name": "Firefly Composite ME", "maker": "adobe", "family": "adobe/firefly-composite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Composite ME is a firefly-composite model. Status on 2026-10-15: active. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3298, "dossier_id": "adobe/firefly-design/firefly-design", "name": "Firefly Design Model", "maker": "adobe", "family": "adobe/firefly-design", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Design Model is a firefly-design model. Status on 2026-10-15: active. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3299, "dossier_id": "adobe/firefly-image/firefly-fill-expand", "name": "Firefly Fill & Expand", "maker": "adobe", "family": "adobe/firefly-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 1.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Fill & Expand is a firefly-image model. Status on 2026-10-15: active. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3300, "dossier_id": "adobe/firefly-image/firefly-image-1", "name": "Firefly Image 1", "maker": "adobe", "family": "adobe/firefly-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Image 1 is a firefly-image model. Status on 2026-10-15: active. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3301, "dossier_id": "adobe/firefly-image/firefly-image-2", "name": "Firefly Image 2", "maker": "adobe", "family": "adobe/firefly-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Image 2 is a firefly-image model. Status on 2026-10-15: retired. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3302, "dossier_id": "adobe/firefly-image/firefly-image-3", "name": "Firefly Image 3", "maker": "adobe", "family": "adobe/firefly-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Image 3 is a firefly-image model. Status on 2026-10-15: deprecated. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3303, "dossier_id": "adobe/firefly-image/firefly-image-4", "name": "Firefly Image 4", "maker": "adobe", "family": "adobe/firefly-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Image 4 is a firefly-image model. Status on 2026-10-15: active. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3304, "dossier_id": "adobe/firefly-image/firefly-image-4-ultra", "name": "Firefly Image 4 Ultra", "maker": "adobe", "family": "adobe/firefly-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 20.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Image 4 Ultra is a firefly-image model. Status on 2026-10-15: active. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3305, "dossier_id": "adobe/firefly-image/firefly-image-5", "name": "Firefly Image 5", "maker": "adobe", "family": "adobe/firefly-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Image 5 is a firefly-image model. Status on 2026-10-15: active. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3306, "dossier_id": "adobe/firefly-upscale/firefly-upscale", "name": "Firefly Precise Upsampler", "maker": "adobe", "family": "adobe/firefly-upscale", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Precise Upsampler is a firefly-upscale model. Status on 2026-10-15: active. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3307, "dossier_id": "adobe/firefly-vector/firefly-vector", "name": "Firefly Vector Model", "maker": "adobe", "family": "adobe/firefly-vector", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Vector Model is a firefly-vector model. Status on 2026-10-15: active. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3308, "dossier_id": "adobe/firefly-video/firefly-video", "name": "Firefly Video Model", "maker": "adobe", "family": "adobe/firefly-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Adobe Firefly Video Model is a firefly-video model. Status on 2026-10-15: active. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3309, "dossier_id": "agh/bielik/bielik-11b-v3", "name": "Bielik-11B-v3", "maker": "agh", "family": "agh/bielik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3310, "dossier_id": "agh/bielik/bielik-11b-v3-instruct", "name": "Bielik-11B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3311, "dossier_id": "agh/bielik/bielik-minitron-7b-v3-instruct", "name": "Bielik-Minitron-7B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3312, "dossier_id": "agh/bielik/bielik-pl-11b-v3-instruct", "name": "Bielik-PL-11B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3313, "dossier_id": "agh/bielik/bielik-pl-minitron-7b-v3-instruct", "name": "Bielik-PL-Minitron-7B-v3.0-Instruct", "maker": "agh", "family": "agh/bielik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller Polish-tokenizer instruct model. The report compares it with the original-tokenizer Minitron and with the PL 11B model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3314, "dossier_id": "ai21/task-specific/contextual-answers", "name": "Contextual Answers", "maker": "ai21", "family": "ai21/task-specific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3315, "dossier_id": "ai21/task-specific/grammatical-error-correction", "name": "Grammatical Error Correction", "maker": "ai21", "family": "ai21/task-specific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3316, "dossier_id": "ai21/task-specific/paraphrase", "name": "Paraphrase", "maker": "ai21", "family": "ai21/task-specific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3317, "dossier_id": "ai21/task-specific/text-improvements", "name": "Text Improvements", "maker": "ai21", "family": "ai21/task-specific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3318, "dossier_id": "ai21/task-specific/text-segmentation", "name": "Text Segmentation", "maker": "ai21", "family": "ai21/task-specific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3319, "dossier_id": "aisingapore/dudi/llama3-2-1b-dudi", "name": "Llama3.2-1B-DuDi", "maker": "aisingapore", "family": "aisingapore/dudi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research student model showing DuDi distillation for Southeast Asian languages. It is not part of the SEA-LION product catalogue.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3320, "dossier_id": "aisingapore/dudi/qwen2-5-0-5b-dudi", "name": "Qwen2.5-0.5B-DuDi", "maker": "aisingapore", "family": "aisingapore/dudi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research student model showing DuDi distillation for Southeast Asian languages. It is not part of the SEA-LION product catalogue.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3321, "dossier_id": "aisingapore/dudi/qwen2-5-1-5b-dudi", "name": "Qwen2.5-1.5B-DuDi", "maker": "aisingapore", "family": "aisingapore/dudi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research student model showing DuDi distillation for Southeast Asian languages. It is not part of the SEA-LION product catalogue.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3322, "dossier_id": "aisingapore/dudi/qwen3-0-6b-base-dudi", "name": "Qwen3-0.6B-Base-DuDi", "maker": "aisingapore", "family": "aisingapore/dudi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research student model showing DuDi distillation for Southeast Asian languages. It is not part of the SEA-LION product catalogue.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3323, "dossier_id": "aisingapore/research/coherence-momentum", "name": "Coherence Momentum", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Coherence model checkpoint using Wall Street Journal data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3324, "dossier_id": "aisingapore/research/cross-sentence-gec", "name": "Cross-sentence GEC", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Grammatical error classification checkpoint using Lang-8, NUCLE, and CoNLL-2014. License was not in the Hub tags.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3325, "dossier_id": "aisingapore/research/lif3wayap", "name": "Lif3WayAp", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Conversational QA checkpoint using QuAC.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3326, "dossier_id": "aisingapore/research/lsr", "name": "LSR", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Document-level relation extraction checkpoint using DocRED.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3327, "dossier_id": "aisingapore/research/roberta-base", "name": "RoBERTa-base", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "AI Singapore text-classification checkpoint for RECCON. This is not the original Facebook RoBERTa-base release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3328, "dossier_id": "aisingapore/research/rst-pointer", "name": "RST-pointer", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "RST discourse parsing checkpoint trained with the RST Discourse Treebank.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3329, "dossier_id": "aisingapore/research/rumour-detection-twitter", "name": "Rumour Detection Twitter", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Rumour detection checkpoint using Twitter15, Twitter16, and PHEME.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3330, "dossier_id": "aisingapore/research/senticgcn", "name": "SenticGCN", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Aspect sentiment checkpoint using SemEval sets. It is AI Singapore's released SenticGCN checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3331, "dossier_id": "aisingapore/research/spanbert", "name": "SPANBert", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3332, "dossier_id": "aisingapore/research/unsupervised-feature-decomposition", "name": "Unsupervised Feature Decomposition", "maker": "aisingapore", "family": "aisingapore/research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-classification checkpoint for unsupervised feature decomposition.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3333, "dossier_id": "aisingapore/sea-embedding/sea-lion-modernbert-300m", "name": "SEA-LION-ModernBERT-300M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fill-mask, classification, and continued training. It is not the contrastive embedding model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3334, "dossier_id": "aisingapore/sea-embedding/sea-lion-modernbert-600m", "name": "SEA-LION-ModernBERT-600M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fill-mask and classification encoder, and the base for the 600M embedding tune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3335, "dossier_id": "aisingapore/sea-embedding/sea-lion-modernbert-embedding-300m", "name": "SEA-LION-ModernBERT-Embedding-300M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval, RAG, and similarity in Southeast Asian languages at the smaller ModernBERT size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3336, "dossier_id": "aisingapore/sea-embedding/sea-lion-modernbert-embedding-600m", "name": "SEA-LION-ModernBERT-Embedding-600M", "maker": "aisingapore", "family": "aisingapore/sea-embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual embeddings for RAG, search, and sentence similarity across Southeast Asian languages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3337, "dossier_id": "aisingapore/sea-lion/apertus-sea-lion-v4-8b-it", "name": "Apertus-SEA-LION-v4-8B-IT", "maker": "aisingapore", "family": "aisingapore/sea-lion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Transparent Southeast Asian instruct model for chat, translation, and tool calling, continued from the Swiss AI Apertus 8B instruct checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3338, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multimodal agentic chat, tool calling, and reasoning in English and Southeast Asian languages, with a configurable thinking mode.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3339, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3340, "dossier_id": "aisingapore/sea-lion-bert/sealion-bert-large", "name": "SEA-LION-BERT-Large", "maker": "aisingapore", "family": "aisingapore/sea-lion-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fill-mask encoder for the same v1 language list as the base BERT.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3341, "dossier_id": "aleph-alpha/luminous/luminous-base", "name": "Luminous-Base", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3342, "dossier_id": "aleph-alpha/luminous/luminous-base-control", "name": "Luminous-Base-Control", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3343, "dossier_id": "aleph-alpha/luminous/luminous-explore", "name": "Luminous-Explore", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3344, "dossier_id": "aleph-alpha/luminous/luminous-extended", "name": "Luminous-Extended", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3345, "dossier_id": "aleph-alpha/luminous/luminous-extended-control", "name": "Luminous-Extended-Control", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3346, "dossier_id": "aleph-alpha/luminous/luminous-supreme", "name": "Luminous-Supreme", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3347, "dossier_id": "aleph-alpha/luminous/luminous-supreme-control", "name": "Luminous-Supreme-Control", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3348, "dossier_id": "aleph-alpha/luminous/luminous-world", "name": "Luminous-World", "maker": "aleph-alpha", "family": "aleph-alpha/luminous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3349, "dossier_id": "aleph-alpha/magma/magma", "name": "MAGMA", "maker": "aleph-alpha", "family": "aleph-alpha/magma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3350, "dossier_id": "aleph-alpha/tfree/llama-tfree-hat-pretrained-7b-dpo", "name": "Llama-TFree-HAT-Pretrained-7B-DPO", "maker": "aleph-alpha", "family": "aleph-alpha/tfree", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3351, "dossier_id": "aleph-alpha/tfree/tfree-hat-pretrained-7b-base", "name": "TFree-HAT-Pretrained-7B-Base", "maker": "aleph-alpha", "family": "aleph-alpha/tfree", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3352, "dossier_id": "alibaba/decision/decision-model-preview", "name": "Decision-Model-Preview", "maker": "alibaba", "family": "alibaba/decision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preview decision model on Model Studio, separate from the chat Qwen line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3353, "dossier_id": "alibaba/qwen/qwen-72b", "name": "Qwen-72B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Original Qwen 72B base model, the earliest large Qwen checkpoint on the retrieved Hugging Face list.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3354, "dossier_id": "alibaba/qwen/qwen2-1-5b", "name": "Qwen2-1.5B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned Qwen2 1.5B model. Predecessor line to Qwen2.5.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3355, "dossier_id": "alibaba/qwen/qwen2-5-0-5b", "name": "Qwen2.5-0.5B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "0.5B Qwen2.5 instruct model.", "summary_intro": "Alibaba model Qwen2.5-0.5B-Instruct. Status: active. Collected 2026-10-15. 0.5B Qwen2.5 instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3356, "dossier_id": "alibaba/qwen/qwen2-5-1-5b", "name": "Qwen2.5-1.5B-Instruct", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.5B Qwen2.5 instruct model.", "summary_intro": "Alibaba model Qwen2.5-1.5B-Instruct. Status: active. Collected 2026-10-15. 1.5B Qwen2.5 instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3357, "dossier_id": "alibaba/qwen/qwen3-0-6b", "name": "Qwen3-0.6B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest instruction-tuned model in the April 2025 Qwen3 release.", "summary_intro": "Alibaba model Qwen3-0.6B. Status: active. Collected 2026-10-15. Smallest instruction-tuned model in the April 2025 Qwen3 release.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3358, "dossier_id": "alibaba/qwen/qwen3-1-7b", "name": "Qwen3-1.7B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.7B instruction-tuned Qwen3 model.", "summary_intro": "Alibaba model Qwen3-1.7B. Status: active. Collected 2026-10-15. 1.7B instruction-tuned Qwen3 model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3359, "dossier_id": "alibaba/qwen/qwen3-4b", "name": "Qwen3-4B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "4B instruction-tuned Qwen3 model. Later 2507 instruct and thinking checkpoints exist as separate repos.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3360, "dossier_id": "alibaba/qwen/qwen3-5-0-8b", "name": "Qwen3.5-0.8B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest named Qwen3.5 open model in the March 2026 small-model release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3361, "dossier_id": "alibaba/qwen/qwen3-5-2b", "name": "Qwen3.5-2B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Qwen3.5 multimodal model.", "summary_intro": "Alibaba model Qwen3.5-2B. Status: active. Collected 2026-10-15. Small Qwen3.5 multimodal model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3362, "dossier_id": "alibaba/qwen/qwen3-5-4b", "name": "Qwen3.5-4B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Qwen3.5 multimodal model, released with the 9B, 2B, and 0.8B models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3363, "dossier_id": "alibaba/qwen/qwen3-6-27b", "name": "Qwen3.6-27B", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3364, "dossier_id": "alibaba/qwen/qwen3-8-flash-next", "name": "Qwen3.8-Flash-Next", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-weight Qwen3.8 Flash-Next checkpoint published on the Qwen Hugging Face org.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3365, "dossier_id": "alibaba/qwen/qwen3-max", "name": "Qwen3-Max", "maker": "alibaba", "family": "alibaba/qwen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3366, "dossier_id": "alibaba/qwen-audio/qwen-audio-3-0-tts-plus", "name": "Qwen-Audio-3.0-TTS-Plus", "maker": "alibaba", "family": "alibaba/qwen-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hosted Qwen text-to-speech model recommended for audiobooks, broadcasts, and avatars.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3367, "dossier_id": "alibaba/qwen-audio/qwen-audio-3-1-asr", "name": "Qwen-Audio-3.1-ASR-Flash", "maker": "alibaba", "family": "alibaba/qwen-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3368, "dossier_id": "alibaba/qwen-audio/qwen-audio-3-1-realtime-plus", "name": "Qwen-Audio-3.1-Realtime-Plus", "maker": "alibaba", "family": "alibaba/qwen-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3369, "dossier_id": "alibaba/qwen-audio/qwen3-asr-0-6b", "name": "Qwen3-ASR-0.6B", "maker": "alibaba", "family": "alibaba/qwen-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 0.6B Qwen3 ASR model.", "summary_intro": "Alibaba model Qwen3-ASR-0.6B. Status: active. Collected 2026-10-15. Open 0.6B Qwen3 ASR model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3370, "dossier_id": "alibaba/qwen-audio/qwen3-asr-1-7b", "name": "Qwen3-ASR-1.7B", "maker": "alibaba", "family": "alibaba/qwen-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 1.7B automatic speech recognition model. Paper tag arXiv 2601.21337.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3371, "dossier_id": "alibaba/qwen-audio/qwen3-tts-0-6b", "name": "Qwen3-TTS-12Hz-0.6B", "maker": "alibaba", "family": "alibaba/qwen-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3372, "dossier_id": "alibaba/qwen-audio/qwen3-tts-1-7b", "name": "Qwen3-TTS-12Hz-1.7B", "maker": "alibaba", "family": "alibaba/qwen-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3373, "dossier_id": "alibaba/qwen-coder/qwen-coder-plus", "name": "Qwen-Coder-Plus", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3374, "dossier_id": "alibaba/qwen-coder/qwen-coder-turbo", "name": "Qwen-Coder-Turbo", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3375, "dossier_id": "alibaba/qwen-coder/qwen2-5-coder-1-5b", "name": "Qwen2.5-Coder-1.5B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.5B Qwen2.5 coder instruct model.", "summary_intro": "Alibaba model Qwen2.5-Coder-1.5B-Instruct. Status: active. Collected 2026-10-15. 1.5B Qwen2.5 coder instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3376, "dossier_id": "alibaba/qwen-coder/qwen2-5-coder-14b", "name": "Qwen2.5-Coder-14B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "14B Qwen2.5 coder instruct model.", "summary_intro": "Alibaba model Qwen2.5-Coder-14B-Instruct. Status: active. Collected 2026-10-15. 14B Qwen2.5 coder instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3377, "dossier_id": "alibaba/qwen-coder/qwen2-5-coder-32b", "name": "Qwen2.5-Coder-32B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest open Qwen2.5 coder instruct model on the retrieved list.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3378, "dossier_id": "alibaba/qwen-coder/qwen2-5-coder-3b", "name": "Qwen2.5-Coder-3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "3B Qwen2.5 coder instruct model. HF license tag is other.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3379, "dossier_id": "alibaba/qwen-coder/qwen2-5-coder-7b", "name": "Qwen2.5-Coder-7B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B Qwen2.5 coder instruct model.", "summary_intro": "Alibaba model Qwen2.5-Coder-7B-Instruct. Status: active. Collected 2026-10-15. 7B Qwen2.5 coder instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3380, "dossier_id": "alibaba/qwen-coder/qwen3-coder-480b-a35b", "name": "Qwen3-Coder-480B-A35B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3381, "dossier_id": "alibaba/qwen-embed/gte-rerank", "name": "GTE-Rerank", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3382, "dossier_id": "alibaba/qwen-embed/qwen3-embedding-0-6b", "name": "Qwen3-Embedding-0.6B", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 0.6B Qwen3 embedding model. Paper tag arXiv 2506.05176.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3383, "dossier_id": "alibaba/qwen-embed/qwen3-embedding-4b", "name": "Qwen3-Embedding-4B", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 4B Qwen3 embedding model.", "summary_intro": "Alibaba model Qwen3-Embedding-4B. Status: active. Collected 2026-10-15. Open 4B Qwen3 embedding model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3384, "dossier_id": "alibaba/qwen-embed/qwen3-embedding-8b", "name": "Qwen3-Embedding-8B", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 8B Qwen3 embedding model.", "summary_intro": "Alibaba model Qwen3-Embedding-8B. Status: active. Collected 2026-10-15. Open 8B Qwen3 embedding model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3385, "dossier_id": "alibaba/qwen-embed/qwen3-reranker-0-6b", "name": "Qwen3-Reranker-0.6B", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 0.6B Qwen3 reranker. HF pipeline tag is text-ranking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3386, "dossier_id": "alibaba/qwen-embed/qwen3-reranker-4b", "name": "Qwen3-Reranker-4B", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 4B Qwen3 reranker.", "summary_intro": "Alibaba model Qwen3-Reranker-4B. Status: active. Collected 2026-10-15. Open 4B Qwen3 reranker.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3387, "dossier_id": "alibaba/qwen-embed/qwen3-vl-embedding-2b", "name": "Qwen3-VL-Embedding-2B", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 2B multimodal embedding based on Qwen3-VL-2B-Instruct.", "summary_intro": "Alibaba model Qwen3-VL-Embedding-2B. Status: active. Collected 2026-10-15. Open 2B multimodal embedding based on Qwen3-VL-2B-Instruct.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3388, "dossier_id": "alibaba/qwen-embed/qwen3-vl-embedding-8b", "name": "Qwen3-VL-Embedding-8B", "maker": "alibaba", "family": "alibaba/qwen-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open multimodal embedding based on Qwen3-VL-8B-Instruct. Paper tag arXiv 2601.04720.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3389, "dossier_id": "alibaba/qwen-image/qwen-image", "name": "Qwen-Image", "maker": "alibaba", "family": "alibaba/qwen-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open text-to-image model. The card stresses text rendering, including Chinese. Hugging Face lists about 20B parameters.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3390, "dossier_id": "alibaba/qwen-image/qwen-image-2-1", "name": "Qwen-Image-2.1", "maker": "alibaba", "family": "alibaba/qwen-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open Qwen image model for generation and editing, including native transparency. Commercial use needs a separate license.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3391, "dossier_id": "alibaba/qwen-image/qwen-image-2-1-pro", "name": "Qwen-Image-2.1-Pro", "maker": "alibaba", "family": "alibaba/qwen-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3392, "dossier_id": "alibaba/qwen-image/qwen-image-2-1-turbo", "name": "Qwen-Image-2.1-Turbo", "maker": "alibaba", "family": "alibaba/qwen-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3393, "dossier_id": "alibaba/qwen-image/qwen-image-3-0-pro", "name": "Qwen-Image-3.0-Pro", "maker": "alibaba", "family": "alibaba/qwen-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Recommended hosted image model on the Model Studio models page, alongside Wan2.7-Image-Pro.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3394, "dossier_id": "alibaba/qwen-image/qwen-image-edit-2509", "name": "Qwen-Image-Edit-2509", "maker": "alibaba", "family": "alibaba/qwen-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3395, "dossier_id": "alibaba/qwen-image/qwen-image-max", "name": "Qwen-Image-Max", "maker": "alibaba", "family": "alibaba/qwen-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3396, "dossier_id": "alibaba/qwen-math/qwen2-5-math-prm-7b", "name": "Qwen2.5-Math-PRM-7B", "maker": "alibaba", "family": "alibaba/qwen-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B process reward model for scoring math reasoning steps. Not a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3397, "dossier_id": "alibaba/qwen-omni/qwen3-5-omni-flash", "name": "Qwen3.5-Omni-Flash", "maker": "alibaba", "family": "alibaba/qwen-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named next to Qwen3.5-Omni-Plus for video with audio input, up to 1 hour and 2 GB.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3398, "dossier_id": "alibaba/qwen-omni/qwen3-8-omni-flash-realtime", "name": "Qwen3.8-Omni-Flash-Realtime", "maker": "alibaba", "family": "alibaba/qwen-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3399, "dossier_id": "alibaba/qwen-omni/qwen3-omni-30b-a3b", "name": "Qwen3-Omni-30B-A3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open omni instruct model. HF pipeline tag is any-to-any. License tag is other.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3400, "dossier_id": "alibaba/qwen-vl/qwen-vl-ocr", "name": "Qwen-VL-OCR", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Older OCR line. The vision page lists qwen-vl-ocr and its snapshots as legacy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3401, "dossier_id": "alibaba/qwen-vl/qwen2-5-vl-32b", "name": "Qwen2.5-VL-32B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "32B Qwen2.5 vision-language instruct model.", "summary_intro": "Alibaba model Qwen2.5-VL-32B-Instruct. Status: active. Collected 2026-10-15. 32B Qwen2.5 vision-language instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3402, "dossier_id": "alibaba/qwen-vl/qwen2-5-vl-3b", "name": "Qwen2.5-VL-3B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "3B Qwen2.5-VL instruct model. The retrieved HF tags did not include a license string.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3403, "dossier_id": "alibaba/qwen-vl/qwen2-5-vl-7b", "name": "Qwen2.5-VL-7B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B Qwen2.5-VL instruct model.", "summary_intro": "Alibaba model Qwen2.5-VL-7B-Instruct. Status: active. Collected 2026-10-15. 7B Qwen2.5-VL instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3404, "dossier_id": "alibaba/qwen-vl/qwen2-vl-2b", "name": "Qwen2-VL-2B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "2B Qwen2-VL instruct model.", "summary_intro": "Alibaba model Qwen2-VL-2B-Instruct. Status: active. Collected 2026-10-15. 2B Qwen2-VL instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3405, "dossier_id": "alibaba/qwen-vl/qwen2-vl-7b", "name": "Qwen2-VL-7B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B Qwen2-VL instruct model.", "summary_intro": "Alibaba model Qwen2-VL-7B-Instruct. Status: active. Collected 2026-10-15. 7B Qwen2-VL instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3406, "dossier_id": "alibaba/qwen-vl/qwen3-5-ocr", "name": "Qwen3.5-OCR", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3407, "dossier_id": "alibaba/qwen-vl/qwen3-vl-2b", "name": "Qwen3-VL-2B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 2B Qwen3-VL instruct model.", "summary_intro": "Alibaba model Qwen3-VL-2B-Instruct. Status: active. Collected 2026-10-15. Open 2B Qwen3-VL instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3408, "dossier_id": "alibaba/qwen-vl/qwen3-vl-4b", "name": "Qwen3-VL-4B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 4B Qwen3-VL instruct model.", "summary_intro": "Alibaba model Qwen3-VL-4B-Instruct. Status: active. Collected 2026-10-15. Open 4B Qwen3-VL instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3409, "dossier_id": "alibaba/qwen-vl/qwen3-vl-8b", "name": "Qwen3-VL-8B-Instruct", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 8B Qwen3 vision-language instruct model.", "summary_intro": "Alibaba model Qwen3-VL-8B-Instruct. Status: active. Collected 2026-10-15. Open 8B Qwen3 vision-language instruct model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3410, "dossier_id": "alibaba/qwen-vl/qwen3-vl-plus", "name": "Qwen3-VL-Plus", "maker": "alibaba", "family": "alibaba/qwen-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3411, "dossier_id": "alibaba/wan/wan2-1", "name": "Wan2.1", "maker": "alibaba", "family": "alibaba/wan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First widely shipped Wan generation line still referenced as a legacy silent model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3412, "dossier_id": "alibaba/wan/wan2-2-i2v", "name": "Wan2.2-I2V", "maker": "alibaba", "family": "alibaba/wan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Wan 2.2 image-to-video. Fine-tune docs also name wan2.2-i2v-flash and wan2.2-kf2v-flash.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3413, "dossier_id": "alibaba/wan/wan2-5-i2v", "name": "Wan2.5-I2V", "maker": "alibaba", "family": "alibaba/wan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Wan 2.5 image-to-video generation with audio.", "summary_intro": "Alibaba model Wan2.5-I2V. Status: active. Collected 2026-10-15. Wan 2.5 image-to-video generation with audio.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3414, "dossier_id": "alibaba/wan/wan2-6-i2v", "name": "Wan2.6-I2V", "maker": "alibaba", "family": "alibaba/wan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3415, "dossier_id": "alibaba/wan/wan2-7-i2v", "name": "Wan2.7-I2V", "maker": "alibaba", "family": "alibaba/wan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3416, "dossier_id": "alibaba/wan/wan2-7-image-pro", "name": "Wan2.7-Image-Pro", "maker": "alibaba", "family": "alibaba/wan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Wan image model recommended next to Qwen-Image-3.0-Pro on the models page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3417, "dossier_id": "alibaba/wan/wan3-0-video", "name": "Wan3.0-Video", "maker": "alibaba", "family": "alibaba/wan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3418, "dossier_id": "alibaba/wan/wan3-0-video-prime", "name": "Wan3.0-Video-Prime", "maker": "alibaba", "family": "alibaba/wan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster Wan3.0 endpoint. Docs say quality stays aligned with the standard model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3419, "dossier_id": "allenai/astabrief/astabrief-8b", "name": "AstaBrief 8B", "maker": "allenai", "family": "allenai/astabrief", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Turns a research question and retrieved excerpts into a cited report. Used as Fast mode in Asta.", "summary_intro": "AstaBrief 8B is Ai2's open scientific report model, Apache-2.0, repo created 2026-02-09. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3420, "dossier_id": "allenai/dolma-classifier/dolma3-quality-classifier", "name": "Dolma 3 quality classifier", "maker": "allenai", "family": "allenai/dolma-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score web text quality for the Dolma 3 mix.", "summary_intro": "The Dolma 3 quality classifier is Ai2's fastText filter for Dolma 3, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3421, "dossier_id": "allenai/dolma-classifier/dolma3-topic-classifier", "name": "Dolma 3 topic classifier", "maker": "allenai", "family": "allenai/dolma-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Topic labels for Dolma 3 data organization.", "summary_intro": "The Dolma 3 topic classifier is Ai2's fastText topic model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3422, "dossier_id": "allenai/dr-tulu/dr-tulu-8b", "name": "DR Tulu 8B", "maker": "allenai", "family": "allenai/dr-tulu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3423, "dossier_id": "allenai/graspmolmo/graspmolmo", "name": "GraspMolmo", "maker": "allenai", "family": "allenai/graspmolmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predicts task-oriented grasps from images and language.", "summary_intro": "GraspMolmo is Ai2's June 2025 grasping model finetuned from Molmo 7B-D, MIT license on the Hub. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3424, "dossier_id": "allenai/layout/hvila-row-docbank", "name": "HVILA row LayoutLM", "maker": "allenai", "family": "allenai/layout", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hierarchical row layout detection.", "summary_intro": "HVILA row LayoutLM is an Ai2 hierarchical layout checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3425, "dossier_id": "allenai/layout/ivila-block-docbank", "name": "IVILA block LayoutLM", "maker": "allenai", "family": "allenai/layout", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Detect block layout in documents.", "summary_intro": "IVILA block LayoutLM is an Ai2 DocBank layout checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3426, "dossier_id": "allenai/layout/ivila-row-s2vl", "name": "IVILA row LayoutLM", "maker": "allenai", "family": "allenai/layout", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Detect row structure in scholarly page images.", "summary_intro": "IVILA row LayoutLM is an Ai2 document-layout checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3427, "dossier_id": "allenai/longformer/longformer-scico", "name": "Longformer SciCo", "maker": "allenai", "family": "allenai/longformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scientific claim comparison classifier.", "summary_intro": "Longformer SciCo is Ai2's SciCo classification model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3428, "dossier_id": "allenai/molmo/molmo-72b", "name": "Molmo 72B", "maker": "allenai", "family": "allenai/molmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest original Molmo model for image understanding and pointing.", "summary_intro": "Molmo 72B is Ai2's September 2024 72B vision-language model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3429, "dossier_id": "allenai/molmo/molmo-7b-o", "name": "Molmo 7B-O", "maker": "allenai", "family": "allenai/molmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fully open 7B vision-language model using an OLMo language backbone, including image pointing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3430, "dossier_id": "allenai/molmo/molmoe-1b", "name": "MolmoE 1B", "maker": "allenai", "family": "allenai/molmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest Molmo model, a mixture-of-experts vision-language model with image pointing.", "summary_intro": "MolmoE 1B is Ai2's September 2024 open vision-language mixture-of-experts model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3431, "dossier_id": "allenai/molmo2/molmo2-4b", "name": "Molmo 2 4B", "maker": "allenai", "family": "allenai/molmo2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Compact Molmo 2 for image, captioning, pointing, video understanding, and tracking.", "summary_intro": "Molmo 2 4B is Ai2's December 2025 efficient open video-language model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3432, "dossier_id": "allenai/molmo2/molmo2-er", "name": "Molmo2 ER", "maker": "allenai", "family": "allenai/molmo2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embodied and spatial reasoning checkpoint built on Molmo 2 4B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3433, "dossier_id": "allenai/molmo2/molmo2-o-7b", "name": "Molmo 2 O 7B", "maker": "allenai", "family": "allenai/molmo2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Molmo 2 with an Olmo language backbone so the language model, not only the vision adapter, is fully open.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3434, "dossier_id": "allenai/molmo2/molmopoint-8b", "name": "MolmoPoint 8B", "maker": "allenai", "family": "allenai/molmo2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized pointing model in the Molmo 2 line.", "summary_intro": "MolmoPoint 8B is Ai2's March 2026 pointing model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3435, "dossier_id": "allenai/molmo2/molmopoint-gui-8b", "name": "MolmoPoint GUI 8B", "maker": "allenai", "family": "allenai/molmo2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pointing model aimed at graphical user interfaces.", "summary_intro": "MolmoPoint GUI 8B is Ai2's March 2026 GUI pointing model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3436, "dossier_id": "allenai/molmo2/molmopoint-vid-4b", "name": "MolmoPoint Vid 4B", "maker": "allenai", "family": "allenai/molmo2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video pointing checkpoint in the MolmoPoint line.", "summary_intro": "MolmoPoint Vid 4B is Ai2's March 2026 video pointing model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3437, "dossier_id": "allenai/molmoact/molmoact-7b-d", "name": "MolmoAct 7B-D", "maker": "allenai", "family": "allenai/molmoact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "MolmoAct 7B-D is Ai2's August 2025 open robotics model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3438, "dossier_id": "allenai/molmoact/molmoact2", "name": "MolmoAct 2", "maker": "allenai", "family": "allenai/molmoact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3439, "dossier_id": "allenai/molmomotion/molmomotion-4b", "name": "MolmoMotion 4B", "maker": "allenai", "family": "allenai/molmomotion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predicts where objects will move. The July 2026 newsletter introduces MolmoMotion.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3440, "dossier_id": "allenai/molmoweb/molmoweb-4b", "name": "MolmoWeb 4B", "maker": "allenai", "family": "allenai/molmoweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open web agent. The April 2026 newsletter introduces MolmoWeb. Card papers arXiv 2601.10611 and 2604.08516.", "summary_intro": "MolmoWeb 4B is an Ai2 open web-agent checkpoint, Apache-2.0, repo created 2026-03-20. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3441, "dossier_id": "allenai/molmoweb/molmoweb-8b", "name": "MolmoWeb 8B", "maker": "allenai", "family": "allenai/molmoweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger MolmoWeb open web agent.", "summary_intro": "MolmoWeb 8B is Ai2's larger open web-agent checkpoint, Apache-2.0, repo created 2026-03-20. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3442, "dossier_id": "allenai/olmo/olmo-1b-0724", "name": "OLMo 1B 0724", "maker": "allenai", "family": "allenai/olmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "July 2024 refresh of the 1B OLMo base model on the Dolma line.", "summary_intro": "OLMo 1B 0724 is Ai2's mid-2024 refresh of the 1B OLMo base model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3443, "dossier_id": "allenai/olmo/olmo-7b-0424", "name": "OLMo 7B 0424", "maker": "allenai", "family": "allenai/olmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "April 2024 improved OLMo 7B base checkpoint.", "summary_intro": "OLMo 7B 0424 is the April 2024 OLMo 7B base refresh from Ai2, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3444, "dossier_id": "allenai/olmo/olmo-7b-0724-instruct", "name": "OLMo 7B 0724 Instruct", "maker": "allenai", "family": "allenai/olmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model on the July 2024 OLMo 7B base, using Tulu v2-style post-training data.", "summary_intro": "OLMo 7B 0724 Instruct is the instruction-tuned July 2024 OLMo 7B from Ai2, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3445, "dossier_id": "allenai/olmo/olmo-7b-instruct", "name": "OLMo 7B Instruct", "maker": "allenai", "family": "allenai/olmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following OLMo 7B tuned with the Tulu 2 SFT mix and a cleaned UltraFeedback preference stage.", "summary_intro": "OLMo 7B Instruct is Ai2's instruction-tuned OLMo 7B, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3446, "dossier_id": "allenai/olmo-2/olmo-2-13b", "name": "OLMo 2 13B", "maker": "allenai", "family": "allenai/olmo-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3447, "dossier_id": "allenai/olmo-2/olmo-2-13b-instruct", "name": "OLMo 2 13B Instruct", "maker": "allenai", "family": "allenai/olmo-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "OLMo 2 13B Instruct is Ai2's post-trained 13B OLMo 2 chat model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3448, "dossier_id": "allenai/olmocr/olmocr-7b", "name": "olmOCR 7B", "maker": "allenai", "family": "allenai/olmocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English document OCR. Later replaced as the current release by olmOCR 2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3449, "dossier_id": "allenai/olmoearth/olmoearth-v1-1-nano", "name": "OlmoEarth v1.1 Nano", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1.1 Nano is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3450, "dossier_id": "allenai/olmoearth/olmoearth-v1-1-tiny", "name": "OlmoEarth v1.1 Tiny", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1.1 Tiny is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3451, "dossier_id": "allenai/olmoearth/olmoearth-v1-2-base", "name": "OlmoEarth v1.2 Base", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1.2 Base is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3452, "dossier_id": "allenai/olmoearth/olmoearth-v1-2-nano", "name": "OlmoEarth v1.2 Nano", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1.2 Nano is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3453, "dossier_id": "allenai/olmoearth/olmoearth-v1-2-small", "name": "OlmoEarth v1.2 Small", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1.2 Small is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3454, "dossier_id": "allenai/olmoearth/olmoearth-v1-2-tiny", "name": "OlmoEarth v1.2 Tiny", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1.2 Tiny is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3455, "dossier_id": "allenai/olmoearth/olmoearth-v1-base", "name": "OlmoEarth v1 Base", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1 Base is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3456, "dossier_id": "allenai/olmoearth/olmoearth-v1-large", "name": "OlmoEarth v1 Large", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1 Large is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3457, "dossier_id": "allenai/olmoearth/olmoearth-v1-nano", "name": "OlmoEarth v1 Nano", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1 Nano is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3458, "dossier_id": "allenai/olmoearth/olmoearth-v1-tiny", "name": "OlmoEarth v1 Tiny", "maker": "allenai", "family": "allenai/olmoearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation foundation model for downstream maps such as crops, mangroves, and fire fuel.", "summary_intro": "OlmoEarth v1 Tiny is an Ai2 OlmoEarth foundation checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3459, "dossier_id": "allenai/primera/primera", "name": "PRIMERA", "maker": "allenai", "family": "allenai/primera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Summarize clusters of documents.", "summary_intro": "PRIMERA is Ai2's multi-document summarizer. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3460, "dossier_id": "allenai/primera/primera-multinews", "name": "PRIMERA Multi-News", "maker": "allenai", "family": "allenai/primera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-news summarization checkpoint.", "summary_intro": "PRIMERA Multi-News is Ai2's Multi-News finetune of PRIMERA. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3461, "dossier_id": "allenai/roberta/biomed-roberta-base", "name": "Biomed RoBERTa base", "maker": "allenai", "family": "allenai/roberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomedical domain language model.", "summary_intro": "Biomed RoBERTa base is Ai2's biomedical RoBERTa checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3462, "dossier_id": "allenai/roberta/cs-roberta-base", "name": "CS RoBERTa base", "maker": "allenai", "family": "allenai/roberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Computer-science domain language model.", "summary_intro": "CS RoBERTa base is Ai2's computer-science RoBERTa checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3463, "dossier_id": "allenai/scibert/scibert-cased", "name": "SciBERT cased", "maker": "allenai", "family": "allenai/scibert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cased twin of SciBERT for scientific text.", "summary_intro": "SciBERT cased is the cased Ai2 SciBERT checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3464, "dossier_id": "allenai/scibert/scibert-uncased", "name": "SciBERT uncased", "maker": "allenai", "family": "allenai/scibert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scientific-domain masked language model used for classification and tagging.", "summary_intro": "SciBERT uncased is Ai2's scientific BERT with a domain vocabulary. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3465, "dossier_id": "allenai/sera/sera-14b", "name": "SERA 14B", "maker": "allenai", "family": "allenai/sera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mid-size SERA coding agent announced on 2026-02-03.", "summary_intro": "SERA 14B is an Ai2 Open Coding Agents model, Apache-2.0 unless noted. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3466, "dossier_id": "allenai/specter/specter", "name": "SPECTER", "maker": "allenai", "family": "allenai/specter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embeddings of scientific papers for relatedness and citation-style retrieval.", "summary_intro": "SPECTER is Ai2's scientific paper embedding model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3467, "dossier_id": "allenai/specter/specter2", "name": "SPECTER2", "maker": "allenai", "family": "allenai/specter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Successor scientific embeddings with task adapters, including ad-hoc query.", "summary_intro": "SPECTER2 is Ai2's 2023 scientific embedding model with adapters, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3468, "dossier_id": "allenai/tk-instruct/tk-instruct-large-def-pos", "name": "Tk-Instruct large def-pos", "maker": "allenai", "family": "allenai/tk-instruct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger Tk-Instruct checkpoint for instruction following.", "summary_intro": "Tk-Instruct large def-pos is an Ai2 instruction-tuned T5-large, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3469, "dossier_id": "allenai/tk-instruct/tk-instruct-small-def-pos", "name": "Tk-Instruct small def-pos", "maker": "allenai", "family": "allenai/tk-instruct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Follow natural-language task instructions.", "summary_intro": "Tk-Instruct small def-pos is an Ai2 instruction-tuned T5-small, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3470, "dossier_id": "allenai/tmax/tmax-27b", "name": "TMax 27B", "maker": "allenai", "family": "allenai/tmax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open terminal agent from the TMax recipe and the TMax-15k environment set.", "summary_intro": "TMax 27B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3471, "dossier_id": "allenai/tmax/tmax-2b", "name": "TMax 2B", "maker": "allenai", "family": "allenai/tmax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open terminal agent from the TMax recipe and the TMax-15k environment set.", "summary_intro": "TMax 2B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3472, "dossier_id": "allenai/tmax/tmax-4b", "name": "TMax 4B", "maker": "allenai", "family": "allenai/tmax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open terminal agent from the TMax recipe and the TMax-15k environment set.", "summary_intro": "TMax 4B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3473, "dossier_id": "allenai/tmax/tmax-8b", "name": "TMax 8B", "maker": "allenai", "family": "allenai/tmax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "TMax 8B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3474, "dossier_id": "allenai/tmax/tmax-9b", "name": "TMax 9B", "maker": "allenai", "family": "allenai/tmax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open terminal agent from the TMax recipe and the TMax-15k environment set.", "summary_intro": "TMax 9B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3475, "dossier_id": "allenai/tmax/tmax-sft-8b", "name": "TMax SFT 8B", "maker": "allenai", "family": "allenai/tmax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "TMax SFT 8B is an Ai2 terminal-agent model, Apache-2.0, from the June 2026 TMax release. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3476, "dossier_id": "allenai/tulu/llama-3-1-tulu-2-70b", "name": "Llama 3.1 Tulu 2 70B", "maker": "allenai", "family": "allenai/tulu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tulu 2 data mix applied to Llama 3.1 70B. The card also reports a DPO sibling.", "summary_intro": "Llama 3.1 Tulu 2 70B is an August 2024 Ai2 instruction model on Llama 3.1 70B. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3477, "dossier_id": "allenai/tulu/llama-3-tulu-2-8b", "name": "Llama 3 Tulu 2 8B", "maker": "allenai", "family": "allenai/tulu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tulu 2 recipe applied to Llama 3 8B.", "summary_intro": "Llama 3 Tulu 2 8B is a June 2024 Ai2 instruction model on Llama 3 8B. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3478, "dossier_id": "allenai/tulu/tulu-3-1-8b", "name": "Tulu 3.1 8B", "maker": "allenai", "family": "allenai/tulu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tulu 3.1 8B refresh. OLMo 2 32B's blog says its post-training used Tulu 3.1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3479, "dossier_id": "allenai/tulu/tulu-3-405b", "name": "Tulu 3 405B", "maker": "allenai", "family": "allenai/tulu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3480, "dossier_id": "allenai/tulu/tulu-3-70b", "name": "Tulu 3 70B", "maker": "allenai", "family": "allenai/tulu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3481, "dossier_id": "allenai/tulu/tulu-3-8b-rm", "name": "Tulu 3 8B Reward Model", "maker": "allenai", "family": "allenai/tulu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preference reward model for the Tulu 3 recipe. The 70B and 405B cards point at this same RM.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3482, "dossier_id": "allenai/tulu/tulu-v1-pythia-6-9b", "name": "Tulu v1 Pythia 6.9B", "maker": "allenai", "family": "allenai/tulu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Early Tulu instruction model from the Open Instruct release, before Tulu 2.", "summary_intro": "Tulu v1 Pythia 6.9B is an early Ai2 instruction finetune of Pythia 6.9B. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3483, "dossier_id": "allenai/unifiedqa/unifiedqa-t5-11b", "name": "UnifiedQA T5 11b", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA T5 11b is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3484, "dossier_id": "allenai/unifiedqa/unifiedqa-t5-3b", "name": "UnifiedQA T5 3b", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA T5 3b is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3485, "dossier_id": "allenai/unifiedqa/unifiedqa-t5-base", "name": "UnifiedQA T5 base", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA T5 base is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3486, "dossier_id": "allenai/unifiedqa/unifiedqa-t5-large", "name": "UnifiedQA T5 large", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA T5 large is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3487, "dossier_id": "allenai/unifiedqa/unifiedqa-t5-small", "name": "UnifiedQA T5 small", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA T5 small is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3488, "dossier_id": "allenai/unifiedqa/unifiedqa-v2-t5-11b", "name": "UnifiedQA v2 T5 11b", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA v2 T5 11b is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3489, "dossier_id": "allenai/unifiedqa/unifiedqa-v2-t5-3b", "name": "UnifiedQA v2 T5 3b", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA v2 T5 3b is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3490, "dossier_id": "allenai/unifiedqa/unifiedqa-v2-t5-base", "name": "UnifiedQA v2 T5 base", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA v2 T5 base is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3491, "dossier_id": "allenai/unifiedqa/unifiedqa-v2-t5-large", "name": "UnifiedQA v2 T5 large", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA v2 T5 large is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3492, "dossier_id": "allenai/unifiedqa/unifiedqa-v2-t5-small", "name": "UnifiedQA v2 T5 small", "maker": "allenai", "family": "allenai/unifiedqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions when the question format is mixed into one text-to-text task.", "summary_intro": "UnifiedQA v2 T5 small is an Ai2 UnifiedQA checkpoint. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3493, "dossier_id": "allenai/wildguard/wildguard", "name": "WildGuard", "maker": "allenai", "family": "allenai/wildguard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open moderator for harmful user requests and model responses, trained with WildGuardMix.", "summary_intro": "WildGuard is Ai2's June 2024 open safety model, Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3494, "dossier_id": "amazon/nova/nova-2-5-sonic", "name": "Amazon Nova 2.5 Sonic", "maker": "amazon", "family": "amazon/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3495, "dossier_id": "amazon/nova/nova-2-omni", "name": "Amazon Nova 2 Omni", "maker": "amazon", "family": "amazon/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3496, "dossier_id": "amazon/nova/nova-2-pro", "name": "Amazon Nova 2 Pro", "maker": "amazon", "family": "amazon/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3497, "dossier_id": "amazon/nova/nova-canvas", "name": "Amazon Nova Canvas", "maker": "amazon", "family": "amazon/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3498, "dossier_id": "amazon/nova/nova-reel", "name": "Amazon Nova Reel", "maker": "amazon", "family": "amazon/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "AWS describes short professional-style videos from text or image prompts, with camera-motion controls, for media and marketing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3499, "dossier_id": "amazon/titan/titan-image-generator-g1", "name": "Amazon Titan Image Generator G1", "maker": "amazon", "family": "amazon/titan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3500, "dossier_id": "amazon/titan/titan-image-generator-g1-v2", "name": "Amazon Titan Image Generator G1 v2", "maker": "amazon", "family": "amazon/titan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3501, "dossier_id": "amazon/titan/titan-text-g1-large", "name": "Amazon Titan Text G1 - Large", "maker": "amazon", "family": "amazon/titan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An early Titan text-generation id. The current user guide does not describe a separate job for it.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3502, "dossier_id": "amazon/titan/titan-text-lite", "name": "Amazon Titan Text G1 - Lite", "maker": "amazon", "family": "amazon/titan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3503, "dossier_id": "american/morphable/rock", "name": "Rock Model", "maker": "american", "family": "american/morphable", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3504, "dossier_id": "american/morphable/toy", "name": "Squishy Toy Model", "maker": "american", "family": "american/morphable", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3505, "dossier_id": "american/tag/tag", "name": "Translucent Appearance Generation (TAG)", "maker": "american", "family": "american/tag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3506, "dossier_id": "amsterdam/crystalite/crystalite", "name": "Crystalite", "maker": "amsterdam", "family": "amsterdam/crystalite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3507, "dossier_id": "amsterdam/mosaic/mosaic", "name": "Mosaic", "maker": "amsterdam", "family": "amsterdam/mosaic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3508, "dossier_id": "anthropic/claude/claude-1", "name": "Claude 1", "maker": "anthropic", "family": "anthropic/claude", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3509, "dossier_id": "anu/e2e-vae/e2e-flux-vae", "name": "E2E-FLUX-VAE", "maker": "anu", "family": "anu/e2e-vae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3510, "dossier_id": "anu/e2e-vae/e2e-in-vae", "name": "E2E-IN-VAE", "maker": "anu", "family": "anu/e2e-vae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3511, "dossier_id": "anu/e2e-vae/e2e-qwen-image-vae", "name": "E2E-Qwen-Image-VAE", "maker": "anu", "family": "anu/e2e-vae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3512, "dossier_id": "anu/e2e-vae/e2e-sd-3-5-vae", "name": "E2E-SD-3.5-VAE", "maker": "anu", "family": "anu/e2e-vae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3513, "dossier_id": "anu/e2e-vae/e2e-sd-vae", "name": "E2E-SD-VAE", "maker": "anu", "family": "anu/e2e-vae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3514, "dossier_id": "anu/e2e-vae/e2e-va-vae", "name": "E2E-VA-VAE", "maker": "anu", "family": "anu/e2e-vae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3515, "dossier_id": "anu/in-vae/in-vae", "name": "IN-VAE", "maker": "anu", "family": "anu/in-vae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3516, "dossier_id": "anu/minidog/minidog", "name": "MiniDog", "maker": "anu", "family": "anu/minidog", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3517, "dossier_id": "anu/repa-e/sit-xl-1-e2e-vavae", "name": "SiT-XL/1 E2E-VA-VAE", "maker": "anu", "family": "anu/repa-e", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3518, "dossier_id": "anu/repa-e/sit-xl-1-repae-invae", "name": "SiT-XL/1 REPA-E IN-VAE", "maker": "anu", "family": "anu/repa-e", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3519, "dossier_id": "anu/repa-e/sit-xl-1-repae-vavae", "name": "SiT-XL/1 REPA-E VA-VAE", "maker": "anu", "family": "anu/repa-e", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3520, "dossier_id": "anu/repa-e/sit-xl-2-repae-sdvae", "name": "SiT-XL/2 REPA-E SD-VAE", "maker": "anu", "family": "anu/repa-e", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3521, "dossier_id": "apple/4m/4m-21", "name": "4M-21", "maker": "apple", "family": "apple/4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predict across tens of vision tasks and modalities, including text co-training.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3522, "dossier_id": "apple/4m/4m-7", "name": "4M-7", "maker": "apple", "family": "apple/4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Any-to-any prediction across a small set of vision modalities.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3523, "dossier_id": "apple/adm/adm-3-cloud", "name": "ADM 3 Cloud", "maker": "apple", "family": "apple/adm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image Playground, Genmoji, photo editing including Spatial Reframing, and other image generation and editing features.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3524, "dossier_id": "apple/afm/afm-3-cloud-pro", "name": "AFM 3 Cloud Pro", "maker": "apple", "family": "apple/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3525, "dossier_id": "apple/afm/afm-3-core", "name": "AFM 3 Core", "maker": "apple", "family": "apple/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General on-device text and image-understanding tasks in the third-generation Apple Intelligence stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3526, "dossier_id": "apple/afm/afm-3-core-advanced", "name": "AFM 3 Core Advanced", "maker": "apple", "family": "apple/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3527, "dossier_id": "apple/afm/afm-on-device", "name": "AFM-on-device", "maker": "apple", "family": "apple/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3528, "dossier_id": "apple/afm/afm-server", "name": "AFM-server", "maker": "apple", "family": "apple/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Apple says AFM-server handles Apple Intelligence requests that run on Private Cloud Compute rather than on device.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3529, "dossier_id": "apple/afm/afm-server-2025", "name": "AFM server (2025)", "maker": "apple", "family": "apple/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3530, "dossier_id": "apple/aim/aim-1b", "name": "AIM-1B", "maker": "apple", "family": "apple/aim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision representation learning and ImageNet classification with a frozen trunk.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3531, "dossier_id": "apple/aim/aim-3b", "name": "AIM-3B", "maker": "apple", "family": "apple/aim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision representation learning and ImageNet classification with a frozen trunk.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3532, "dossier_id": "apple/aim/aim-600m", "name": "AIM-600M", "maker": "apple", "family": "apple/aim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision representation learning and ImageNet classification with a frozen trunk.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3533, "dossier_id": "apple/aim/aim-7b", "name": "AIM-7B", "maker": "apple", "family": "apple/aim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision representation learning and ImageNet classification with a frozen trunk.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3534, "dossier_id": "apple/aimv2/aimv2-1b", "name": "AIMv2-1B", "maker": "apple", "family": "apple/aimv2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction. A paired text tower exists for the large LIT checkpoint, cataloged separately.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3535, "dossier_id": "apple/aimv2/aimv2-3b", "name": "AIMv2-3B", "maker": "apple", "family": "apple/aimv2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction. A paired text tower exists for the large LIT checkpoint, cataloged separately.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3536, "dossier_id": "apple/aimv2/aimv2-huge", "name": "AIMv2-huge", "maker": "apple", "family": "apple/aimv2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction. A paired text tower exists for the large LIT checkpoint, cataloged separately.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3537, "dossier_id": "apple/aimv2/aimv2-large", "name": "AIMv2-large", "maker": "apple", "family": "apple/aimv2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction. A paired text tower exists for the large LIT checkpoint, cataloged separately.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3538, "dossier_id": "apple/cadd/cadd-base-7b", "name": "CADD-Base-7B", "maker": "apple", "family": "apple/cadd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation research. The card says the continuous signal can sit on top of several unmasking algorithms.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3539, "dossier_id": "apple/clara/clara-7b-base", "name": "CLaRa-7B-Base", "maker": "apple", "family": "apple/clara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval-augmented question answering with documents compressed into continuous latents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3540, "dossier_id": "apple/clara/clara-7b-e2e", "name": "CLaRa-7B-E2E", "maker": "apple", "family": "apple/clara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval-augmented question answering with documents compressed into continuous latents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3541, "dossier_id": "apple/clara/clara-7b-instruct", "name": "CLaRa-7B-Instruct", "maker": "apple", "family": "apple/clara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval-augmented question answering with documents compressed into continuous latents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3542, "dossier_id": "apple/depth-pro/depth-pro", "name": "Depth Pro", "maker": "apple", "family": "apple/depth-pro", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sharp high-resolution metric depth from a single image, plus focal-length estimation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3543, "dossier_id": "apple/dfn/dfn2b-clip-vit-b-16", "name": "DFN2B-CLIP-ViT-B-16", "maker": "apple", "family": "apple/dfn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3544, "dossier_id": "apple/dfn/dfn2b-clip-vit-l-14", "name": "DFN2B-CLIP-ViT-L-14", "maker": "apple", "family": "apple/dfn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3545, "dossier_id": "apple/dfn/dfn2b-clip-vit-l-14-39b", "name": "DFN2B-CLIP-ViT-L-14-39B", "maker": "apple", "family": "apple/dfn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3546, "dossier_id": "apple/dfn/dfn5b-clip-vit-h-14", "name": "DFN5B-CLIP-ViT-H-14", "maker": "apple", "family": "apple/dfn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3547, "dossier_id": "apple/dfn/dfn5b-clip-vit-h-14-378", "name": "DFN5B-CLIP-ViT-H-14-378", "maker": "apple", "family": "apple/dfn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image classification and retrieval. DFN is the data-filtering method used to build the training pool.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3548, "dossier_id": "apple/fastvit/fastvit-ma36", "name": "FastViT-MA36", "maker": "apple", "family": "apple/fastvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3549, "dossier_id": "apple/fastvit/fastvit-s12", "name": "FastViT-S12", "maker": "apple", "family": "apple/fastvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3550, "dossier_id": "apple/fastvit/fastvit-sa12", "name": "FastViT-SA12", "maker": "apple", "family": "apple/fastvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3551, "dossier_id": "apple/fastvit/fastvit-sa24", "name": "FastViT-SA24", "maker": "apple", "family": "apple/fastvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3552, "dossier_id": "apple/fastvit/fastvit-sa36", "name": "FastViT-SA36", "maker": "apple", "family": "apple/fastvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3553, "dossier_id": "apple/fastvit/fastvit-t12", "name": "FastViT-T12", "maker": "apple", "family": "apple/fastvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3554, "dossier_id": "apple/fastvit/fastvit-t8", "name": "FastViT-T8", "maker": "apple", "family": "apple/fastvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile image classification. The hybrid encoder later feeds MobileCLIP and FastVLM.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3555, "dossier_id": "apple/mobileclip/mobileclip-b", "name": "MobileCLIP-B", "maker": "apple", "family": "apple/mobileclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3556, "dossier_id": "apple/mobileclip/mobileclip-b-lt", "name": "MobileCLIP-B (LT)", "maker": "apple", "family": "apple/mobileclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3557, "dossier_id": "apple/mobileclip/mobileclip-l-14", "name": "MobileCLIP-L/14", "maker": "apple", "family": "apple/mobileclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3558, "dossier_id": "apple/mobileclip/mobileclip-s0", "name": "MobileCLIP-S0", "maker": "apple", "family": "apple/mobileclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3559, "dossier_id": "apple/mobileclip/mobileclip-s1", "name": "MobileCLIP-S1", "maker": "apple", "family": "apple/mobileclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3560, "dossier_id": "apple/mobileclip/mobileclip-s2", "name": "MobileCLIP-S2", "maker": "apple", "family": "apple/mobileclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3561, "dossier_id": "apple/mobileclip/mobileclip-s3", "name": "MobileCLIP-S3", "maker": "apple", "family": "apple/mobileclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3562, "dossier_id": "apple/mobileclip/mobileclip-s4", "name": "MobileCLIP-S4", "maker": "apple", "family": "apple/mobileclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3563, "dossier_id": "apple/mobileclip2/mobileclip2-b", "name": "MobileCLIP2-B", "maker": "apple", "family": "apple/mobileclip2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3564, "dossier_id": "apple/mobileclip2/mobileclip2-l-14", "name": "MobileCLIP2-L/14", "maker": "apple", "family": "apple/mobileclip2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3565, "dossier_id": "apple/mobileclip2/mobileclip2-s0", "name": "MobileCLIP2-S0", "maker": "apple", "family": "apple/mobileclip2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3566, "dossier_id": "apple/mobileclip2/mobileclip2-s2", "name": "MobileCLIP2-S2", "maker": "apple", "family": "apple/mobileclip2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3567, "dossier_id": "apple/mobileclip2/mobileclip2-s3", "name": "MobileCLIP2-S3", "maker": "apple", "family": "apple/mobileclip2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3568, "dossier_id": "apple/mobileclip2/mobileclip2-s4", "name": "MobileCLIP2-S4", "maker": "apple", "family": "apple/mobileclip2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3569, "dossier_id": "apple/mobileone/mobileone-s0", "name": "MobileOne-S0", "maker": "apple", "family": "apple/mobileone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3570, "dossier_id": "apple/mobileone/mobileone-s1", "name": "MobileOne-S1", "maker": "apple", "family": "apple/mobileone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3571, "dossier_id": "apple/mobileone/mobileone-s2", "name": "MobileOne-S2", "maker": "apple", "family": "apple/mobileone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3572, "dossier_id": "apple/mobileone/mobileone-s3", "name": "MobileOne-S3", "maker": "apple", "family": "apple/mobileone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3573, "dossier_id": "apple/mobileone/mobileone-s4", "name": "MobileOne-S4", "maker": "apple", "family": "apple/mobileone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device image classification and a backbone for detection and segmentation.", "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%.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3574, "dossier_id": "apple/mobilevit/deeplabv3-mobilevit-small", "name": "DeepLabV3-MobileViT-S", "maker": "apple", "family": "apple/mobilevit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Semantic segmentation, with Pascal VOC named as the dataset on the Hub.", "summary_intro": "DeepLabV3-MobileViT-S is Apple's MobileViT semantic-segmentation checkpoint on Hugging Face, released May 2022. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3575, "dossier_id": "apple/mobilevit/deeplabv3-mobilevit-x-small", "name": "DeepLabV3-MobileViT-XS", "maker": "apple", "family": "apple/mobilevit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Semantic segmentation, with Pascal VOC named as the dataset on the Hub.", "summary_intro": "DeepLabV3-MobileViT-XS is Apple's MobileViT semantic-segmentation checkpoint on Hugging Face, released May 2022. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3576, "dossier_id": "apple/mobilevit/deeplabv3-mobilevit-xx-small", "name": "DeepLabV3-MobileViT-XXS", "maker": "apple", "family": "apple/mobilevit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Semantic segmentation, with Pascal VOC named as the dataset on the Hub.", "summary_intro": "DeepLabV3-MobileViT-XXS is Apple's MobileViT semantic-segmentation checkpoint on Hugging Face, released May 2022. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3577, "dossier_id": "apple/mobilevit/mobilevit-small", "name": "MobileViT-S", "maker": "apple", "family": "apple/mobilevit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile-friendly image classification.", "summary_intro": "MobileViT-S is Apple's MobileViT image classifier released on Hugging Face, ImageNet-1k, 256px. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3578, "dossier_id": "apple/mobilevit/mobilevit-x-small", "name": "MobileViT-XS", "maker": "apple", "family": "apple/mobilevit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile-friendly image classification.", "summary_intro": "MobileViT-XS is Apple's MobileViT image classifier released on Hugging Face, ImageNet-1k, 256px. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3579, "dossier_id": "apple/mobilevit/mobilevit-xx-small", "name": "MobileViT-XXS", "maker": "apple", "family": "apple/mobilevit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile-friendly image classification.", "summary_intro": "MobileViT-XXS is Apple's MobileViT image classifier released on Hugging Face, ImageNet-1k, 256px. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3580, "dossier_id": "apple/mobilevitv2/mobilevitv2-1-0", "name": "MobileViTv2-1.0", "maker": "apple", "family": "apple/mobilevitv2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification or semantic segmentation, depending on the checkpoint.", "summary_intro": "MobileViTv2-1.0 is an Apple MobileViTv2 checkpoint on Hugging Face, June 2023. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3581, "dossier_id": "apple/mobilevitv2/mobilevitv2-1-0-voc", "name": "MobileViTv2-1.0-VOC", "maker": "apple", "family": "apple/mobilevitv2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification or semantic segmentation, depending on the checkpoint.", "summary_intro": "MobileViTv2-1.0-VOC is an Apple MobileViTv2 checkpoint on Hugging Face, June 2023. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3582, "dossier_id": "apple/mobilevitv2/mobilevitv2-1-5-voc", "name": "MobileViTv2-1.5-VOC", "maker": "apple", "family": "apple/mobilevitv2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification or semantic segmentation, depending on the checkpoint.", "summary_intro": "MobileViTv2-1.5-VOC is an Apple MobileViTv2 checkpoint on Hugging Face, June 2023. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3583, "dossier_id": "apple/mobilevitv2/mobilevitv2-2-0", "name": "MobileViTv2-2.0", "maker": "apple", "family": "apple/mobilevitv2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification or semantic segmentation, depending on the checkpoint.", "summary_intro": "MobileViTv2-2.0 is an Apple MobileViTv2 checkpoint on Hugging Face, June 2023. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3584, "dossier_id": "apple/sage/sage-ft-mixtral-8x7b", "name": "SAGE-FT-Mixtral-8x7B", "maker": "apple", "family": "apple/sage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research on emotionally steered, strategy-aware multi-turn dialogue.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3585, "dossier_id": "apple/sharp/sharp", "name": "SHARP", "maker": "apple", "family": "apple/sharp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Photorealistic nearby novel views from a single image, with metric scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3586, "dossier_id": "apple/simplesd/simplesd-30b-instruct", "name": "SimpleSD-30B-instruct", "maker": "apple", "family": "apple/simplesd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation research. The method fine-tunes a model on its own sampled solutions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3587, "dossier_id": "apple/starflow/starflow", "name": "STARFlow", "maker": "apple", "family": "apple/starflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation with a transformer autoregressive flow.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3588, "dossier_id": "apple/starflow/starflow-v", "name": "STARFlow-V", "maker": "apple", "family": "apple/starflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-video generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3589, "dossier_id": "apple/tic-clip/tic-clip", "name": "TiC-CLIP", "maker": "apple", "family": "apple/tic-clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research on continual learning of CLIP over time-indexed DataComp data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3590, "dossier_id": "apptek/asr/asr", "name": "AppTek ASR", "maker": "apptek", "family": "apptek/asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3591, "dossier_id": "apptek/nmt/nmt", "name": "AppTek Neural Machine Translation", "maker": "apptek", "family": "apptek/nmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3592, "dossier_id": "apptek/speech-translation/direct-speech-translation", "name": "AppTek Direct Speech Translation", "maker": "apptek", "family": "apptek/speech-translation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3593, "dossier_id": "apptek/speech-translation/speech-translation", "name": "AppTek Speech Translation", "maker": "apptek", "family": "apptek/speech-translation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3594, "dossier_id": "apptek/tts/expressive-tts", "name": "AppTek Expressive TTS", "maker": "apptek", "family": "apptek/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3595, "dossier_id": "apptek/tts/neural-tts", "name": "AppTek Neural TTS", "maker": "apptek", "family": "apptek/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3596, "dossier_id": "arcee/coder/coder-large", "name": "Coder-Large", "maker": "arcee", "family": "arcee/coder", "context_length": null, "prompt_price_per_m": 0.5, "completion_price_per_m": 0.8, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation, refactoring, and debugging.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3597, "dossier_id": "arcee/coder/coder-small", "name": "Coder-Small", "maker": "arcee", "family": "arcee/coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lighter code generation, review, and prototypes, according to the Conductor page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3598, "dossier_id": "arcee/genesis/genesis-science-1", "name": "Genesis-Science-1", "maker": "arcee", "family": "arcee/genesis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long scientific computing workflows such as HPC code, simulation, materials, and energy systems, with people still approving safety-relevant actions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3599, "dossier_id": "arcee/maestro/maestro-reasoning", "name": "Maestro Reasoning", "maker": "arcee", "family": "arcee/maestro", "context_length": null, "prompt_price_per_m": 0.9, "completion_price_per_m": 3.3, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Step-by-step enterprise reasoning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3600, "dossier_id": "arcee/meraj/meraj", "name": "Arcee Meraj", "maker": "arcee", "family": "arcee/meraj", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Enterprise Arabic generation, according to the Meraj-Mini card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3601, "dossier_id": "arcee/spotlight/spotlight", "name": "Spotlight", "maker": "arcee", "family": "arcee/spotlight", "context_length": null, "prompt_price_per_m": 0.18, "completion_price_per_m": 0.18, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast image-text tasks such as visual question answering, charts, and UI reading.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3602, "dossier_id": "arizona/calibrefine/calibrefine", "name": "CalibRefine", "maker": "arizona", "family": "arizona/calibrefine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3603, "dossier_id": "arizona/transrad/transrad", "name": "TransRAD", "maker": "arizona", "family": "arizona/transrad", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3604, "dossier_id": "arizona/xcalib/crlite", "name": "CRLite", "maker": "arizona", "family": "arizona/xcalib", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3605, "dossier_id": "assemblyai/conformer/conformer-1", "name": "Conformer-1", "maker": "assemblyai", "family": "assemblyai/conformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3606, "dossier_id": "assemblyai/conformer/conformer-2", "name": "Conformer-2", "maker": "assemblyai", "family": "assemblyai/conformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3607, "dossier_id": "assemblyai/medical/medical-v1", "name": "Medical Mode", "maker": "assemblyai", "family": "assemblyai/medical", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3608, "dossier_id": "assemblyai/medical/medical-v2", "name": "Medical Mode v2", "maker": "assemblyai", "family": "assemblyai/medical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "AssemblyAI describes it only as the next Medical Mode for clinical transcription.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3609, "dossier_id": "assemblyai/slam/slam-1", "name": "SLAM-1", "maker": "assemblyai", "family": "assemblyai/slam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3610, "dossier_id": "assemblyai/tts/tts-1", "name": "TTS-1", "maker": "assemblyai", "family": "assemblyai/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3611, "dossier_id": "assemblyai/universal/best", "name": "Best", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.37, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3612, "dossier_id": "assemblyai/universal/nano", "name": "Nano", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.12, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It was the cheap tier for drafts, topic detection, and trying the API. It was not the accuracy model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3613, "dossier_id": "assemblyai/universal/universal-1", "name": "Universal-1", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3614, "dossier_id": "assemblyai/universal/universal-2", "name": "Universal-2", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3615, "dossier_id": "assemblyai/universal/universal-3-5-pro", "name": "Universal-3.5 Pro", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.21, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3616, "dossier_id": "assemblyai/universal/universal-3-5-pro-streaming", "name": "Universal-3.5 Pro Streaming", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.45, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3617, "dossier_id": "assemblyai/universal/universal-3-6", "name": "Universal-3.6", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.3, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "AssemblyAI describes it only as a lower-cost streaming option beside the Pro model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3618, "dossier_id": "assemblyai/universal/universal-3-6-pro", "name": "Universal-3.6 Pro Streaming", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.45, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3619, "dossier_id": "assemblyai/universal/universal-3-6-pro-preview", "name": "Universal-3.6 Pro Preview", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3620, "dossier_id": "assemblyai/universal/universal-3-pro", "name": "Universal-3 Pro", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.21, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3621, "dossier_id": "assemblyai/universal/universal-3-pro-streaming", "name": "Universal-3 Pro Streaming", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.45, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3622, "dossier_id": "assemblyai/universal/universal-4-pro", "name": "Universal-4 Pro", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "AssemblyAI describes it as the next major model for noise, interruptions, hesitation, and accented speech in voice agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3623, "dossier_id": "assemblyai/universal/universal-streaming-english", "name": "Universal-Streaming English", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3624, "dossier_id": "assemblyai/universal/universal-streaming-multilingual", "name": "Universal-Streaming Multilingual", "maker": "assemblyai", "family": "assemblyai/universal", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3625, "dossier_id": "assemblyai/veterinary/veterinary-v1", "name": "Veterinary Mode", "maker": "assemblyai", "family": "assemblyai/veterinary", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "AssemblyAI lists it as a veterinary domain mode, parallel to Medical Mode.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3626, "dossier_id": "asu/eclipse/eclipse-kandinsky", "name": "ECLIPSE Kandinsky Prior", "maker": "asu", "family": "asu/eclipse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3627, "dossier_id": "asu/eclipse/eclipse-karlo", "name": "ECLIPSE Karlo Prior", "maker": "asu", "family": "asu/eclipse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3628, "dossier_id": "asu/eclipse/lambda-eclipse", "name": "\u03bb-ECLIPSE", "maker": "asu", "family": "asu/eclipse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3629, "dossier_id": "asu/tripletclip/tripletclip", "name": "TripletCLIP", "maker": "asu", "family": "asu/tripletclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3630, "dossier_id": "auburn/cspws/cspws-unet-attn", "name": "csPWS U-Net + Attention", "maker": "auburn", "family": "auburn/cspws", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3631, "dossier_id": "baai/altclip/altclip", "name": "AltCLIP", "maker": "baai", "family": "baai/altclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image classification in Chinese and English.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3632, "dossier_id": "baai/altdiffusion/altdiffusion", "name": "AltDiffusion", "maker": "baai", "family": "baai/altdiffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese and English text-to-image generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3633, "dossier_id": "baai/aquila/aquila-135m", "name": "Aquila-135M", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tiny base model for research and on-device experiments.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3634, "dossier_id": "baai/aquila/aquila-135m-instruct", "name": "Aquila-135M-Instruct", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction following at the 135M scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3635, "dossier_id": "baai/aquila/aquila-33b", "name": "Aquila-33B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger Aquila base model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3636, "dossier_id": "baai/aquila/aquila-7b", "name": "Aquila-7B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base model for continuation and further tuning. Not a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3637, "dossier_id": "baai/aquila/aquila-vl-2b", "name": "Aquila-VL-2B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Visual question answering and image-text chat at about 2B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3638, "dossier_id": "baai/aquila/aquila2-70b-expr", "name": "Aquila2-70B-Expr", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Experimental larger Aquila2 base model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3639, "dossier_id": "baai/aquila/aquila2-7b", "name": "Aquila2-7B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base continuation model at 7B in the Aquila2 line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3640, "dossier_id": "baai/aquila/aquilachat-7b", "name": "AquilaChat-7B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese-English chat and tool calling on the Aquila-7B base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3641, "dossier_id": "baai/aquila/aquilachat2-34b", "name": "AquilaChat2-34B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model paired with Aquila2-34B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3642, "dossier_id": "baai/aquila/aquilachat2-70b-expr", "name": "AquilaChat2-70B-Expr", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Experimental 70B-class Aquila2 chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3643, "dossier_id": "baai/aquila/aquilachat2-7b", "name": "AquilaChat2-7B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat model for the Aquila2-7B line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3644, "dossier_id": "baai/aquila/aquilacode-multi", "name": "AquilaCode-multi", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-language code model in the Aquila code line, under the published repo name.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3645, "dossier_id": "baai/aquila/aquilacode-py", "name": "AquilaCode-py", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation on an Aquila backbone. The py suffix is the published repo name.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3646, "dossier_id": "baai/aquila/aquiladense-16b", "name": "AquilaDense-16B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger dense Aquila model from the June 2024 release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3647, "dossier_id": "baai/aquila/aquiladense-7b", "name": "AquilaDense-7B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Dense 7B bilingual base or chat model in the June 2024 Aquila dense line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3648, "dossier_id": "baai/aquila/aquilamed-rl", "name": "AquilaMed-RL", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical question answering and dialogue.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3649, "dossier_id": "baai/aquila/aquilamoe", "name": "AquilaMoE", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sparse bilingual language model, with an SFT checkpoint for chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3650, "dossier_id": "baai/aquila/aquilasql-7b", "name": "AquilaSQL-7B", "maker": "baai", "family": "baai/aquila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-SQL on a 7B Aquila model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3651, "dossier_id": "baai/arex/arex-base", "name": "AREX-Base", "maker": "baai", "family": "baai/arex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Large mixture-of-experts AREX agent.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3652, "dossier_id": "baai/arex/arex-turbo", "name": "AREX-Turbo", "maker": "baai", "family": "baai/arex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller long-horizon agent in the AREX line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3653, "dossier_id": "baai/bge/bge-multilingual-gemma2", "name": "bge-multilingual-gemma2", "maker": "baai", "family": "baai/bge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3654, "dossier_id": "baai/bge/bge-reasoner-embed-qwen3-8b-0923", "name": "bge-reasoner-embed-qwen3-8b-0923", "maker": "baai", "family": "baai/bge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning-style text embedding on a Qwen3 backbone. Exact task instructions are the FlagEmbedding Instruct/Query template.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3655, "dossier_id": "baai/bge/bge-visualized", "name": "Visualized-BGE", "maker": "baai", "family": "baai/bge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embeddings for mixed image and text, on top of the BGE text models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3656, "dossier_id": "baai/bge-vl/bge-vl-base", "name": "BGE-VL-base", "maker": "baai", "family": "baai/bge-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Any visual search: text-to-image, image-to-text, and composed image-plus-text queries.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3657, "dossier_id": "baai/bge-vl/bge-vl-large", "name": "BGE-VL-large", "maker": "baai", "family": "baai/bge-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same universal multimodal retrieval job as BGE-VL-base, at large-CLIP size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3658, "dossier_id": "baai/bge-vl/bge-vl-mllm-s1", "name": "BGE-VL-MLLM-S1", "maker": "baai", "family": "baai/bge-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Composed multimodal retrieval with an MLLM backbone, stage 1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3659, "dossier_id": "baai/bge-vl/bge-vl-mllm-s2", "name": "BGE-VL-MLLM-S2", "maker": "baai", "family": "baai/bge-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Later MLLM stage of BGE-VL for multimodal retrieval.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3660, "dossier_id": "baai/bge-vl/bge-vl-screenshot", "name": "BGE-VL-Screenshot", "maker": "baai", "family": "baai/bge-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multimodal retrieval over screenshots and UI images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3661, "dossier_id": "baai/bge-vl/bge-vl-v1-5", "name": "BGE-VL v1.5", "maker": "baai", "family": "baai/bge-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot and MMEB-oriented multimodal embedding checkpoints.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3662, "dossier_id": "baai/brainmu/brainmu-spike", "name": "Brainmu Spike", "maker": "baai", "family": "baai/brainmu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reconstruct images from spike-camera events.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3663, "dossier_id": "baai/bunny/bunny-llama-3-8b-v", "name": "Bunny-Llama-3-8B-V", "maker": "baai", "family": "baai/bunny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image question answering on a small language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3664, "dossier_id": "baai/bunny/bunny-v1-0-2b-zh", "name": "Bunny-v1.0-2B-zh", "maker": "baai", "family": "baai/bunny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image question answering on a small language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3665, "dossier_id": "baai/bunny/bunny-v1-0-3b", "name": "Bunny-v1.0-3B", "maker": "baai", "family": "baai/bunny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image question answering on a small language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3666, "dossier_id": "baai/bunny/bunny-v1-0-3b-zh", "name": "Bunny-v1.0-3B-zh", "maker": "baai", "family": "baai/bunny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image question answering on a small language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3667, "dossier_id": "baai/bunny/bunny-v1-0-4b", "name": "Bunny-v1.0-4B", "maker": "baai", "family": "baai/bunny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image question answering on a small language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3668, "dossier_id": "baai/bunny/bunny-v1-1-4b", "name": "Bunny-v1.1-4B", "maker": "baai", "family": "baai/bunny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image question answering on a small language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3669, "dossier_id": "baai/bunny/bunny-v1-1-llama-3-8b-v", "name": "Bunny-v1.1-Llama-3-8B-V", "maker": "baai", "family": "baai/bunny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image question answering on a small language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3670, "dossier_id": "baai/consispace/consispace", "name": "ConsiSpace", "maker": "baai", "family": "baai/consispace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Spatial vision-language modeling. The name is the only task hint that was verified.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3671, "dossier_id": "baai/diva/diva", "name": "DIVA", "maker": "baai", "family": "baai/diva", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A stronger CLIP visual encoder for downstream MLLMs and segmentation, without a paired text retrain.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3672, "dossier_id": "baai/emu/emu", "name": "Emu", "maker": "baai", "family": "baai/emu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Any-to-any vision-language generation. Exact tasks were not re-read from the 2023 card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3673, "dossier_id": "baai/emu/emu2", "name": "Emu2", "maker": "baai", "family": "baai/emu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multimodal base model used to build Emu2-Chat and Emu2-Gen.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3674, "dossier_id": "baai/emu/emu2-chat", "name": "Emu2-Chat", "maker": "baai", "family": "baai/emu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language chat on the Emu2 line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3675, "dossier_id": "baai/emu/emu2-gen", "name": "Emu2-Gen", "maker": "baai", "family": "baai/emu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image generation on the Emu2 line.", "summary_intro": "Emu2-Gen is the generation checkpoint of Emu2, created 19 December 2023. Status: active. Confidence: medium. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3676, "dossier_id": "baai/emu/emu3", "name": "Emu3", "maker": "baai", "family": "baai/emu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3677, "dossier_id": "baai/emu/emu3-5-image", "name": "Emu3.5-Image", "maker": "baai", "family": "baai/emu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-image generation and editing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3678, "dossier_id": "baai/emu/emu3-chat", "name": "Emu3-Chat", "maker": "baai", "family": "baai/emu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and video understanding by next-token prediction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3679, "dossier_id": "baai/emu/emu3-gen", "name": "Emu3-Gen", "maker": "baai", "family": "baai/emu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image and video generation by next vision token.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3680, "dossier_id": "baai/eva/eva-clip-18b", "name": "EVA-CLIP-18B", "maker": "baai", "family": "baai/eva", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest EVA-CLIP zero-shot vision-language model in this release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3681, "dossier_id": "baai/eva/eva-clip-8b", "name": "EVA-CLIP-8B", "maker": "baai", "family": "baai/eva", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image and video classification and retrieval.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3682, "dossier_id": "baai/eve/eve-7b", "name": "EVE-7B", "maker": "baai", "family": "baai/eve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language generation. Exact capability list was not read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3683, "dossier_id": "baai/eve/eve-7b-hd", "name": "EVE-7B-HD", "maker": "baai", "family": "baai/eve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-resolution vision-language modeling than EVE-7B v1.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3684, "dossier_id": "baai/judgelm/judgelm-13b", "name": "JudgeLM-13B-v1.0", "maker": "baai", "family": "baai/judgelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score or compare LLM answers for research evaluation. Not a general chat product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3685, "dossier_id": "baai/judgelm/judgelm-33b", "name": "JudgeLM-33B-v1.0", "maker": "baai", "family": "baai/judgelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score or compare LLM answers for research evaluation. Not a general chat product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3686, "dossier_id": "baai/judgelm/judgelm-7b", "name": "JudgeLM-7B-v1.0", "maker": "baai", "family": "baai/judgelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score or compare LLM answers for research evaluation. Not a general chat product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3687, "dossier_id": "baai/mtvcraft/mtvcraft", "name": "MTVCraft", "maker": "baai", "family": "baai/mtvcraft", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate video whose motion follows speech, sound effects, and music.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3688, "dossier_id": "baai/nova/nova", "name": "NOVA", "maker": "baai", "family": "baai/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image at 1024 for the primary checkpoint, and text-to-video on the osp480 checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3689, "dossier_id": "baai/omnigen/omnigen", "name": "OmniGen", "maker": "baai", "family": "baai/omnigen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-model image generation and editing from multimodal prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3690, "dossier_id": "baai/omnigen/omnigen2", "name": "OmniGen2", "maker": "baai", "family": "baai/omnigen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Any-to-any generation in the OmniGen line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3691, "dossier_id": "baai/openseek/openseek-mid", "name": "OpenSeek-Mid-v1", "maker": "baai", "family": "baai/openseek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mid-size OpenSeek language model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3692, "dossier_id": "baai/openseek/openseek-small", "name": "OpenSeek-Small-v1", "maker": "baai", "family": "baai/openseek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open base and SFT language-model checkpoints for the OpenSeek small run.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3693, "dossier_id": "baai/orca/orca-4b", "name": "Orca-4B", "maker": "baai", "family": "baai/orca", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A world latent that can be read out as text, future images, or embodied actions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3694, "dossier_id": "baai/painter/painter", "name": "Painter", "maker": "baai", "family": "baai/painter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "In-context vision tasks that are framed as image-to-image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3695, "dossier_id": "baai/recon2reason/recon2reason-4b", "name": "Recon2Reason-Reasoning-4B", "maker": "baai", "family": "baai/recon2reason", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Spatial reasoning for indoor embodied scenes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3696, "dossier_id": "baai/reranker/bge-reranker-v2-5-gemma2-lightweight", "name": "bge-reranker-v2.5-gemma2-lightweight", "maker": "baai", "family": "baai/reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual reranking when a 9B backbone is too expensive to run at full depth.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3697, "dossier_id": "baai/reranker/bge-reranker-v2-gemma", "name": "bge-reranker-v2-gemma", "maker": "baai", "family": "baai/reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-capacity multilingual reranking than the XLM-RoBERTa rerankers, at Gemma-2B cost.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3698, "dossier_id": "baai/reranker/bge-reranker-v2-minicpm-layerwise", "name": "bge-reranker-v2-minicpm-layerwise", "maker": "baai", "family": "baai/reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual reranking with a selectable compute budget.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3699, "dossier_id": "baai/reranker/llara", "name": "LLaRA", "maker": "baai", "family": "baai/reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Dense retrieval, with separate checkpoints for passage, document, and BEIR settings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3700, "dossier_id": "baai/reranker/matryoshka-reranker", "name": "Matryoshka ReRanker", "maker": "baai", "family": "baai/reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Rerank passages or documents, with a BEIR-oriented checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3701, "dossier_id": "baai/robobrain/gaussianmind", "name": "GaussianMind", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Audio understanding for the embodied line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3702, "dossier_id": "baai/robobrain/robobrain", "name": "RoboBrain", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Robotic planning, affordance, and trajectory prediction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3703, "dossier_id": "baai/robobrain/robobrain-dex", "name": "RoboBrain-Dex", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Dexterous manipulation weights in the RoboBrain line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3704, "dossier_id": "baai/robobrain/robobrain-x0", "name": "RoboBrain-X0", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preview embodied policy or brain. The card was not read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3705, "dossier_id": "baai/robobrain/robobrain2-32b", "name": "RoboBrain2.0-32B", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embodied planning and spatial reasoning at this size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3706, "dossier_id": "baai/robobrain/robobrain2-3b", "name": "RoboBrain2.0-3B", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embodied planning and spatial reasoning at this size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3707, "dossier_id": "baai/robobrain/robobrain2-5-4b", "name": "RoboBrain2.5-4B", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embodied brain at the 4B RoboBrain 2.5 size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3708, "dossier_id": "baai/robobrain/robobrain2-5-8b-mt", "name": "RoboBrain2.5-8B-MT", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "8B embodied checkpoint, MT variant.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3709, "dossier_id": "baai/robobrain/robobrain2-5-8b-nv", "name": "RoboBrain2.5-8B-NV", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "8B embodied checkpoint, NV variant.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3710, "dossier_id": "baai/robobrain/robobrain2-7b", "name": "RoboBrain2.0-7B", "maker": "baai", "family": "baai/robobrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embodied planning and spatial reasoning at this size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3711, "dossier_id": "baai/seggpt/seggpt", "name": "SegGPT", "maker": "baai", "family": "baai/seggpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Promptable image and video segmentation from example masks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3712, "dossier_id": "baai/segvol/segvol", "name": "SegVol", "maker": "baai", "family": "baai/segvol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Volumetric or promptable segmentation. The exact task statement was not read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3713, "dossier_id": "baai/tap/tokenize-anything", "name": "Tokenize Anything", "maker": "baai", "family": "baai/tap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Promptable vision tokenization. Exact outputs were not read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3714, "dossier_id": "baai/tele-flm/tele-flm", "name": "Tele-FLM", "maker": "baai", "family": "baai/tele-flm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open multilingual base model at 52B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3715, "dossier_id": "baai/tele-flm/tele-flm-1t", "name": "Tele-FLM-1T", "maker": "baai", "family": "baai/tele-flm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Very large open multilingual base model for further training and research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3716, "dossier_id": "baai/uni3d/uni3d", "name": "Uni3D", "maker": "baai", "family": "baai/uni3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "3D representation learning. Task details were not read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3717, "dossier_id": "baai/ursa/ursa-0-6b", "name": "URSA-0.6B", "maker": "baai", "family": "baai/ursa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller text-to-video and text-to-image URSA checkpoints.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3718, "dossier_id": "baai/ursa/ursa-1-7b", "name": "URSA-1.7B", "maker": "baai", "family": "baai/ursa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3719, "dossier_id": "baai/video-xl/video-xl-2", "name": "Video-XL-2", "maker": "baai", "family": "baai/video-xl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer questions about long videos.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3720, "dossier_id": "baichuan/baichuan/baichuan-53b", "name": "Baichuan-53B", "maker": "baichuan", "family": "baichuan/baichuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3721, "dossier_id": "baichuan/baichuan-m/baichuan-m1-14b-base", "name": "Baichuan-M1-14B-Base", "maker": "baichuan", "family": "baichuan/baichuan-m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3722, "dossier_id": "baichuan/baichuan-m/baichuan-m1-14b-instruct", "name": "Baichuan-M1-14B-Instruct", "maker": "baichuan", "family": "baichuan/baichuan-m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3723, "dossier_id": "baichuan/baichuan-m/baichuan-m4", "name": "Baichuan-M4", "maker": "baichuan", "family": "baichuan/baichuan-m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3724, "dossier_id": "baichuan/baichuan-med/baichuanmed-ocr-72b", "name": "BaichuanMed-OCR-72B", "maker": "baichuan", "family": "baichuan/baichuan-med", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3725, "dossier_id": "baichuan/baichuan-med/baichuanmed-ocr-7b", "name": "BaichuanMed-OCR-7B", "maker": "baichuan", "family": "baichuan/baichuan-med", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3726, "dossier_id": "baichuan/baichuan-omni/baichuan-omni", "name": "Baichuan-Omni", "maker": "baichuan", "family": "baichuan/baichuan-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3727, "dossier_id": "baichuan/baichuan-omni/baichuan-omni-1-5-base", "name": "Baichuan-Omni-1.5-Base", "maker": "baichuan", "family": "baichuan/baichuan-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3728, "dossier_id": "baichuan/baichuan2/baichuan2-turbo-192k", "name": "Baichuan2-Turbo-192k", "maker": "baichuan", "family": "baichuan/baichuan2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3729, "dossier_id": "baichuan/baichuan3/baichuan3", "name": "Baichuan 3", "maker": "baichuan", "family": "baichuan/baichuan3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3730, "dossier_id": "baichuan/baichuan4/baichuan4-finance", "name": "Baichuan4-Finance", "maker": "baichuan", "family": "baichuan/baichuan4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3731, "dossier_id": "baichuan/npc/baichuan-npc-lite", "name": "Baichuan-NPC-Lite", "maker": "baichuan", "family": "baichuan/npc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3732, "dossier_id": "baichuan/npc/baichuan-npc-turbo", "name": "Baichuan-NPC-Turbo", "maker": "baichuan", "family": "baichuan/npc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3733, "dossier_id": "baidu/ernie/ernie-1-0", "name": "ERNIE 1.0", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese NLU pretraining. The paper's method is knowledge masking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3734, "dossier_id": "baidu/ernie/ernie-2-0", "name": "ERNIE 2.0", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Build richer language representations by adding pretraining tasks over time.", "summary_intro": "ERNIE 2.0 is Baidu's 2019 continual multi-task pretraining framework and model. It is a research release.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3735, "dossier_id": "baidu/ernie/ernie-3-0", "name": "ERNIE 3.0", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Unified understanding and generation pretraining.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3736, "dossier_id": "baidu/ernie/ernie-3-0-titan", "name": "ERNIE 3.0 Titan", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3737, "dossier_id": "baidu/ernie/ernie-3-5", "name": "ERNIE 3.5", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.012, "completion_price_per_m": 0.012, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3738, "dossier_id": "baidu/ernie/ernie-4-0", "name": "ERNIE 4.0", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.12, "completion_price_per_m": 0.12, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3739, "dossier_id": "baidu/ernie/ernie-4-0-turbo", "name": "ERNIE 4.0 Turbo", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same general flagship jobs as ERNIE 4.0, where shorter time-to-first-token mattered.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3740, "dossier_id": "baidu/ernie/ernie-4-5", "name": "ERNIE 4.5", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.004, "completion_price_per_m": 0.016, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3741, "dossier_id": "baidu/ernie/ernie-4-5-0-3b", "name": "ERNIE-4.5-0.3B", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3742, "dossier_id": "baidu/ernie/ernie-4-5-turbo", "name": "ERNIE 4.5 Turbo", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.0008, "completion_price_per_m": 0.0032, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3743, "dossier_id": "baidu/ernie/ernie-4-5-turbo-vl", "name": "ERNIE 4.5 Turbo VL", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.003, "completion_price_per_m": 0.009, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3744, "dossier_id": "baidu/ernie/ernie-4-5-vl-28b-a3b-thinking", "name": "ERNIE-4.5-VL-28B-A3B-Thinking", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3745, "dossier_id": "baidu/ernie/ernie-4-5-vl-424b-a47b", "name": "ERNIE-4.5-VL-424B-A47B", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3746, "dossier_id": "baidu/ernie/ernie-5-0", "name": "ERNIE 5.0", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.006, "completion_price_per_m": 0.024, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3747, "dossier_id": "baidu/ernie/ernie-5-1", "name": "ERNIE 5.1", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.004, "completion_price_per_m": 0.018, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3748, "dossier_id": "baidu/ernie/ernie-bot-turbo", "name": "ERNIE-Bot-turbo", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.008, "completion_price_per_m": 0.008, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster dialogue and generation than the original ERNIE-Bot, including use inside Qianfan data-augmentation jobs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3749, "dossier_id": "baidu/ernie/ernie-character", "name": "ERNIE Character", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Consistent character voice and stronger instruction following than the general chat models, with faster inference.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3750, "dossier_id": "baidu/ernie/ernie-code", "name": "ERNIE Code", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code preference tuning on a 128k Wenxin base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3751, "dossier_id": "baidu/ernie/ernie-functions", "name": "ERNIE Functions", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Function calling. The ledger's suggested replacement was DeepSeek-V3.2, which indicates a tools workload.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3752, "dossier_id": "baidu/ernie/ernie-image", "name": "ERNIE-Image", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3753, "dossier_id": "baidu/ernie/ernie-image-aes", "name": "ERNIE-Image-Aes", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Aesthetic text-to-image. The report pairs it with the ERIA-1K aesthetic benchmark.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3754, "dossier_id": "baidu/ernie/ernie-image-turbo", "name": "ERNIE-Image-Turbo", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster text-to-image with a quality tradeoff Baidu still ranks near the SFT model on GenEval.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3755, "dossier_id": "baidu/ernie/ernie-irag", "name": "ERNIE-iRAG", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image with retrieval. Qianfan's suggested replacement for the first id was Qwen-Image, a third-party model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3756, "dossier_id": "baidu/ernie/ernie-lite", "name": "ERNIE Lite", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat and generation where a flagship was unnecessary, and as a base for private fine-tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3757, "dossier_id": "baidu/ernie/ernie-litev", "name": "ERNIE LiteV", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower-cost image understanding before ERNIE 4.5 Turbo VL.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3758, "dossier_id": "baidu/ernie/ernie-novel", "name": "ERNIE Novel", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-form story writing. The replacement named by Qianfan was ERNIE 4.5 Turbo 32k.", "summary_intro": "ERNIE Novel was Baidu's named fiction model on Qianfan, ERNIE-Novel-8K. It retired on 2025-12-11.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3759, "dossier_id": "baidu/ernie/ernie-speed", "name": "ERNIE Speed", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3760, "dossier_id": "baidu/ernie/ernie-tiny", "name": "ERNIE Tiny", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-volume simple generation and low-cost customization.", "summary_intro": "ERNIE Tiny was Baidu's cheapest Wenxin model to fine-tune and serve. The 8k inference id retired on 2026-01-27.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3761, "dossier_id": "baidu/ernie/ernie-video-1", "name": "ERNIE-Video 1.0", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Short video generation from a prompt or a still.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3762, "dossier_id": "baidu/ernie/ernie-vilg", "name": "ERNIE-ViLG", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese text-to-image creation for non-experts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3763, "dossier_id": "baidu/ernie/ernie-vilg-2", "name": "ERNIE-ViLG 2.0", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese and bilingual text-to-image, with styles exposed in the older PaddleHub client.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3764, "dossier_id": "baidu/ernie/ernie-x1", "name": "ERNIE X1", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.002, "completion_price_per_m": 0.008, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3765, "dossier_id": "baidu/ernie/ernie-x1-1", "name": "ERNIE X1.1", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": 0.001, "completion_price_per_m": 0.004, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Qianfan described gains on QA, tool use, agents, instruction following, logic, math, and code, plus stronger factuality than X1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3766, "dossier_id": "baidu/ernie/ernie-x1-turbo", "name": "ERNIE X1 Turbo", "maker": "baidu", "family": "baidu/ernie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3767, "dossier_id": "baidu/musesteamer/musesteamer-2", "name": "MuseSteamer 2", "maker": "baidu", "family": "baidu/musesteamer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-video, including clips with generated voice and ambient audio on the audio variants.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3768, "dossier_id": "baidu/musesteamer/musesteamer-air-image", "name": "MuseSteamer Air Image", "maker": "baidu", "family": "baidu/musesteamer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cheap text-to-image. Baidu describes it as a price-first model from the search team.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3769, "dossier_id": "baidu/paddleocr/paddleocr-vl", "name": "PaddleOCR-VL", "maker": "baidu", "family": "baidu/paddleocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Parse real-world documents: layout, text, and related page tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3770, "dossier_id": "baidu/qianfan/qianfan-70b", "name": "Qianfan-70B", "maker": "baidu", "family": "baidu/qianfan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger general chat under the Qianfan brand.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3771, "dossier_id": "baidu/qianfan/qianfan-8b", "name": "Qianfan-8B", "maker": "baidu", "family": "baidu/qianfan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General chat at a small size. The ledger's suggested replacement was DeepSeek-V3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3772, "dossier_id": "baidu/qianfan/qianfan-lightning-128b-a19b", "name": "Qianfan-Lightning-128B-A19B", "maker": "baidu", "family": "baidu/qianfan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-context MoE chat. The suggested replacement was DeepSeek-V3.2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3773, "dossier_id": "baidu/qianfan/qianfan-ocr", "name": "Qianfan-OCR", "maker": "baidu", "family": "baidu/qianfan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Read text from images through the chat-style OCR endpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3774, "dossier_id": "baidu/qianfan/qianfan-vl-70b", "name": "Qianfan-VL-70B", "maker": "baidu", "family": "baidu/qianfan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Heavier image understanding under the Qianfan brand.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3775, "dossier_id": "baidu/qianfan/qianfan-vl-8b", "name": "Qianfan-VL-8B", "maker": "baidu", "family": "baidu/qianfan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image understanding at 8B-class size, as labeled.", "summary_intro": "Qianfan-VL-8B was a Baidu vision-language preset. It retired on 2026-06-30. A limit row was not captured.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3776, "dossier_id": "baidu/speech/speech-e2e", "name": "\u7aef\u5230\u7aef\u8bed\u97f3\u8bed\u8a00\u5927\u6a21\u578b", "maker": "baidu", "family": "baidu/speech", "context_length": null, "prompt_price_per_m": 0.064, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Voice agents, companionship, call centers, and education. Flex is the edition that can call tools.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3777, "dossier_id": "bangor/fullstop/fullstop-welsh-punctuation-prediction", "name": "Welsh punctuation restoration", "maker": "bangor", "family": "bangor/fullstop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Restore punctuation on Welsh text, including unpunctuated transcripts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3778, "dossier_id": "bangor/kaldi/kaldi-cy", "name": "Kaldi Welsh (2607)", "maker": "bangor", "family": "bangor/kaldi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-latency Welsh transcription on CPU, including streaming, where a large end-to-end model is too heavy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3779, "dossier_id": "bangor/llama/llama-3-2-1b-welsh-cpt", "name": "Llama 3.2 1B Welsh CPT", "maker": "bangor", "family": "bangor/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not stated beyond the repo name.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3780, "dossier_id": "bangor/llama/llama-3-2-1b-welsh-sft", "name": "Llama 3.2 1B Welsh SFT", "maker": "bangor", "family": "bangor/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3781, "dossier_id": "bangor/llama/llama-3-8b-instruct-cpt-cy", "name": "Llama 3 8B Instruct Welsh CPT", "maker": "bangor", "family": "bangor/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repo id says continued pretraining for Welsh. The card does not state a task beyond a generic chat example.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3782, "dossier_id": "bangor/m2m100/m2m100-1-2b-ft-cy-to-en", "name": "M2M100 1.2B Welsh to English (2510)", "maker": "bangor", "family": "bangor/m2m100", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Welsh-to-English translation. The card does not name a domain.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3783, "dossier_id": "bangor/mt-dspec/mt-dspec-health-en-cy", "name": "Marian Health English to Welsh", "maker": "bangor", "family": "bangor/mt-dspec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3784, "dossier_id": "bangor/mt-dspec/mt-dspec-legislation-en-cy", "name": "Marian Legislation English to Welsh", "maker": "bangor", "family": "bangor/mt-dspec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-to-Welsh translation for legislation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3785, "dossier_id": "bangor/nllb/nllb-200-1-3b-ft-cym-to-eng", "name": "NLLB 1.3B Welsh to English", "maker": "bangor", "family": "bangor/nllb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Welsh-to-English translation. The card gives no domain restriction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3786, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Translate legislation from English into Welsh.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3787, "dossier_id": "bangor/orpheus/orpheus-3b-cy-en", "name": "Orpheus 3B Welsh-English", "maker": "bangor", "family": "bangor/orpheus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3788, "dossier_id": "bangor/piper/cy-en-gb-bu-tts", "name": "Bangor Welsh-English bilingual TTS", "maker": "bangor", "family": "bangor/piper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Screen readers, bilingual language-learning apps, and offline TTS for Welsh, English, and code-switched sentences. Three studio voices.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3789, "dossier_id": "bangor/piper/cy-gb-bu-tts", "name": "cy_GB Bangor Welsh TTS", "maker": "bangor", "family": "bangor/piper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3790, "dossier_id": "bangor/spacy/cy-pos-tagger", "name": "Welsh spaCy part-of-speech tagger", "maker": "bangor", "family": "bangor/spacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Assign Universal Dependencies part-of-speech tags to Welsh tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3791, "dossier_id": "bangor/spacy/cy-tag-lem-ner-lg", "name": "Welsh spaCy tagger, lemmatizer, NER, and vectors", "maker": "bangor", "family": "bangor/spacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Computational processing of Welsh text: syntax tags, dictionary form, entities, and similarity over word vectors.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3792, "dossier_id": "bangor/speecht5/speecht5-finetuned-bu-tts-cy-en", "name": "SpeechT5 Bangor TTS Welsh-English", "maker": "bangor", "family": "bangor/speecht5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not stated on the card. The repo name is a bilingual Bangor TTS fine-tune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3793, "dossier_id": "bangor/truecaser/sacremoses-truecaser-cy", "name": "Welsh Moses truecaser", "maker": "bangor", "family": "bangor/truecaser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3794, "dossier_id": "bangor/wav2vec2/wav2vec-xlsr-ft-cy-verbatim", "name": "wav2vec2 XLSR Verbatim Welsh", "maker": "bangor", "family": "bangor/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repo name says verbatim Welsh recognition. The card body does not state an intended use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3795, "dossier_id": "bangor/wav2vec2/wav2vec2-btb-cv-ft-cv-cy", "name": "wav2vec2 Common Voice Welsh (frozen encoder)", "maker": "bangor", "family": "bangor/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Read or planned Welsh transcription. The collection note says optional KenLM language models give the lowest WER in that set.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3796, "dossier_id": "bangor/wav2vec2/wav2vec2-xlsr-53-ft-btb-cv-cy", "name": "wav2vec2 XLSR Bangor and Common Voice Welsh", "maker": "bangor", "family": "bangor/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3797, "dossier_id": "bangor/wav2vec2/wav2vec2-xlsr-53-ft-cy-en", "name": "wav2vec2 XLSR Welsh-English", "maker": "bangor", "family": "bangor/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Welsh and English speech recognition from spontaneous Bangor Transcription Bank speech and Common Voice 16.1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3798, "dossier_id": "bangor/wav2vec2/wav2vec2-xlsr-53-ft-cy-en-withlm", "name": "wav2vec2 XLSR Welsh-English with KenLM", "maker": "bangor", "family": "bangor/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Welsh and English speech recognition with an n-gram language model at decode time.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3799, "dossier_id": "bangor/whisper/whisper-base-ft-commonvoice-cy-cpp", "name": "Whisper Base Common Voice Welsh (whisper.cpp)", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3800, "dossier_id": "bangor/whisper/whisper-base-ft-commonvoice-cy-en-cpp", "name": "Whisper Base Common Voice Welsh-English (whisper.cpp)", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Offline Welsh and English transcription and language detection on CPU hardware.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3801, "dossier_id": "bangor/whisper/whisper-base-ft-verbatim-cy-en-cpp", "name": "Whisper Base Verbatim Welsh (whisper.cpp)", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Offline verbatim transcription of spontaneous Welsh on desktops, laptops, and phones.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3802, "dossier_id": "bangor/whisper/whisper-large-ft-cy-en", "name": "Whisper Large Welsh and English (2607)", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3803, "dossier_id": "bangor/whisper/whisper-large-v3-ft-commonvoice-cy", "name": "Whisper Large v3 Common Voice Welsh", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3804, "dossier_id": "bangor/whisper/whisper-large-v3-ft-commonvoice-cy-en", "name": "Whisper Large v3 Common Voice Welsh-English", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3805, "dossier_id": "bangor/whisper/whisper-large-v3-ft-verbatim-cy-en", "name": "Whisper Large v3 Verbatim Welsh-English", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Verbatim transcripts of spontaneous or unplanned speech, rather than cleaned read-speech text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3806, "dossier_id": "bangor/whisper/whisper-large-v3-turbo-ft-verbatim-cy-en", "name": "Whisper Large v3 Turbo Verbatim Welsh", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster verbatim transcription of spontaneous Welsh. Training used Banc Trawsgrifiadau Bangor plus Common Voice 18 Welsh.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3807, "dossier_id": "bangor/whisper/whisper-tiny-ft-cy-en", "name": "Whisper Tiny Welsh-English", "maker": "bangor", "family": "bangor/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3808, "dossier_id": "bar-ilan/coresearch/coresearch-reader", "name": "CoReSearch reader", "maker": "bar-ilan", "family": "bar-ilan/coresearch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Read retrieved passages. The card does not describe the output.", "summary_intro": "CoReSearch reader is a RoBERTa reading model from the Bar-Ilan NLP lab, with an empty card. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3809, "dossier_id": "bar-ilan/coresearch/coresearch-retriever", "name": "CoReSearch retriever", "maker": "bar-ilan", "family": "bar-ilan/coresearch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieve passages for the CoReSearch reader. The pairing is inferred from the two repo names, not from a written card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3810, "dossier_id": "bar-ilan/dictalm/dictalm", "name": "DictaLM", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3811, "dossier_id": "bar-ilan/dictalm/dictalm-instruct", "name": "DictaLM-Instruct", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruct-tuned DictaLM. The paper says the fine-tune uses Hebrew question-answering data and a translated MPT instruct set.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3812, "dossier_id": "bar-ilan/dictalm/dictalm-rab", "name": "DictaLM-Rab", "maker": "bar-ilan", "family": "bar-ilan/dictalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Foundation model for rabbinic and historical Hebrew, announced beside DictaLM. The repo id says 7b.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3813, "dossier_id": "bar-ilan/heq/mt5-xl-heq", "name": "mT5-XL HeQ", "maker": "bar-ilan", "family": "bar-ilan/heq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer a Hebrew question conditioned on a context passage.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3814, "dossier_id": "bar-ilan/neodictabert/neodictabert-bilingual-embed", "name": "NeoDictaBERT-bilingual-embed", "maker": "bar-ilan", "family": "bar-ilan/neodictabert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "NeoDictaBERT-bilingual-embed is a Hebrew NeoBERT-style encoder from Dicta, with Bar-Ilan coauthors. Public weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3815, "dossier_id": "bar-ilan/qamden/qamden", "name": "QAmden", "maker": "bar-ilan", "family": "bar-ilan/qamden", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-document modeling by cross-document question answering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3816, "dossier_id": "bar-ilan/qamden/qamden-multinews", "name": "QAmden Multi-News", "maker": "bar-ilan", "family": "bar-ilan/qamden", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repo name says Multi-News. The card does not add a task description beyond the Peek Across citation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3817, "dossier_id": "bar-ilan/text-reduction/led-base-controlled-text-reduction", "name": "Controlled Text Reduction LED-base", "maker": "bar-ilan", "family": "bar-ilan/text-reduction", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "This is the Bar-Ilan NLP lab's LED-base model for controlled text reduction. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3818, "dossier_id": "basel/bfm/bfm-2009", "name": "Basel Face Model 2009", "maker": "basel", "family": "basel/bfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3819, "dossier_id": "basel/bfm/bfm-2017", "name": "Basel Face Model 2017", "maker": "basel", "family": "basel/bfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3820, "dossier_id": "basel/bfm/bfm-2019", "name": "Basel Face Model 2019", "maker": "basel", "family": "basel/bfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3821, "dossier_id": "basque/ahotts/ahotts", "name": "AhoTTS", "maker": "basque", "family": "basque/ahotts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3822, "dossier_id": "basque/bernat/bernat-base", "name": "BERnaT base", "maker": "basque", "family": "basque/bernat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3823, "dossier_id": "basque/bernat/bernat-large", "name": "BERnaT large", "maker": "basque", "family": "basque/bernat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3824, "dossier_id": "basque/bernat/bernat-medium", "name": "BERnaT medium", "maker": "basque", "family": "basque/bernat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3825, "dossier_id": "basque/berteus/berteus", "name": "BERTeus", "maker": "basque", "family": "basque/berteus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3826, "dossier_id": "basque/cap-punct/cap-punct", "name": "HiTZ capitalization and punctuation", "maker": "basque", "family": "basque/cap-punct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3827, "dossier_id": "basque/conformer/bbs-s2tc", "name": "BBS-S2TC Conformer Transducer", "maker": "basque", "family": "basque/conformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3828, "dossier_id": "basque/conformer/stt-eseu-conformer", "name": "Spanish-Basque Conformer Transducer", "maker": "basque", "family": "basque/conformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3829, "dossier_id": "basque/conformer/stt-eu-conformer-ctc", "name": "Basque Conformer CTC", "maker": "basque", "family": "basque/conformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3830, "dossier_id": "basque/conformer/stt-eu-conformer-transducer", "name": "Basque Conformer Transducer", "maker": "basque", "family": "basque/conformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3831, "dossier_id": "basque/gollie/gollie-13b", "name": "GoLLIE 13B", "maker": "basque", "family": "basque/gollie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3832, "dossier_id": "basque/gollie/gollie-34b", "name": "GoLLIE 34B", "maker": "basque", "family": "basque/gollie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3833, "dossier_id": "basque/gollie/gollie-7b", "name": "GoLLIE 7B", "maker": "basque", "family": "basque/gollie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3834, "dossier_id": "basque/guidex/llama-3-1-guidex-8b", "name": "Llama 3.1 GuideX 8B", "maker": "basque", "family": "basque/guidex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3835, "dossier_id": "basque/ixambert/ixambert", "name": "IXAmBERT", "maker": "basque", "family": "basque/ixambert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3836, "dossier_id": "basque/jaunbert/jaunbert", "name": "JaunBERT", "maker": "basque", "family": "basque/jaunbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3837, "dossier_id": "basque/lang-adapt/eu-qwen3-14b", "name": "Basque Qwen3 14B Base", "maker": "basque", "family": "basque/lang-adapt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3838, "dossier_id": "basque/lang-adapt/gl-llama-3-1-8b", "name": "Galician Llama 3.1 8B", "maker": "basque", "family": "basque/lang-adapt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3839, "dossier_id": "basque/latxa/latxa-llama-3-1-70b-instruct", "name": "Latxa Llama 3.1 70B Instruct", "maker": "basque", "family": "basque/latxa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3840, "dossier_id": "basque/latxa/latxa-llama-3-1-8b", "name": "Latxa Llama 3.1 8B", "maker": "basque", "family": "basque/latxa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3841, "dossier_id": "basque/latxa/latxa-llama-3-1-8b-instruct", "name": "Latxa Llama 3.1 8B Instruct", "maker": "basque", "family": "basque/latxa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3842, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3843, "dossier_id": "basque/latxa-vl/latxa-qwen3-5-2b", "name": "Latxa Qwen3.5 2B", "maker": "basque", "family": "basque/latxa-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3844, "dossier_id": "basque/latxa-vl/latxa-qwen3-5-4b", "name": "Latxa Qwen3.5 4B", "maker": "basque", "family": "basque/latxa-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3845, "dossier_id": "basque/latxa-vl/latxa-qwen3-vl-2b-instruct", "name": "Latxa Qwen3-VL 2B Instruct", "maker": "basque", "family": "basque/latxa-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3846, "dossier_id": "basque/latxa-vl/latxa-qwen3-vl-32b-instruct", "name": "Latxa Qwen3-VL 32B Instruct", "maker": "basque", "family": "basque/latxa-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3847, "dossier_id": "basque/latxa-vl/latxa-qwen3-vl-4b-instruct", "name": "Latxa Qwen3-VL 4B Instruct", "maker": "basque", "family": "basque/latxa-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3848, "dossier_id": "basque/latxa-vl/latxa-qwen3-vl-8b-instruct", "name": "Latxa Qwen3-VL 8B Instruct", "maker": "basque", "family": "basque/latxa-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3849, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3850, "dossier_id": "basque/marmoka/marmoka-en", "name": "Marmoka-en", "maker": "basque", "family": "basque/marmoka", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3851, "dossier_id": "basque/marmoka/marmoka-en-it", "name": "Marmoka-en-it", "maker": "basque", "family": "basque/marmoka", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3852, "dossier_id": "basque/marmoka/marmoka-es", "name": "Marmoka-es", "maker": "basque", "family": "basque/marmoka", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3853, "dossier_id": "basque/marmoka/marmoka-es-it", "name": "Marmoka-es-it", "maker": "basque", "family": "basque/marmoka", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3854, "dossier_id": "basque/mt-hitz/mt-hitz", "name": "MT-HiTZ", "maker": "basque", "family": "basque/mt-hitz", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3855, "dossier_id": "basque/pyannote/pyannote-segmentation-rtve", "name": "pyannote segmentation RTVE", "maker": "basque", "family": "basque/pyannote", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3856, "dossier_id": "basque/roberta-eus/roberta-eus-base", "name": "RoBERTa-eus base", "maker": "basque", "family": "basque/roberta-eus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3857, "dossier_id": "basque/roberta-eus/roberta-eus-large", "name": "RoBERTa-eus large", "maker": "basque", "family": "basque/roberta-eus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3858, "dossier_id": "basque/styletts2/styletts2-eu", "name": "StyleTTS2 Basque", "maker": "basque", "family": "basque/styletts2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3859, "dossier_id": "basque/whisper/whisper-eu", "name": "Whisper Basque", "maker": "basque", "family": "basque/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3860, "dossier_id": "bath/crefnet/crefnet", "name": "CRefNet", "maker": "bath", "family": "bath/crefnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3861, "dossier_id": "bath/ovdsgg/ovdsgg", "name": "OvDSGG", "maker": "bath", "family": "bath/ovdsgg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3862, "dossier_id": "berkeley/crossformer/crossformer", "name": "CrossFormer", "maker": "berkeley", "family": "berkeley/crossformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3863, "dossier_id": "berkeley/dextacwam/dextacwam", "name": "DexTacWAM", "maker": "berkeley", "family": "berkeley/dextacwam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3864, "dossier_id": "berkeley/gorilla/gorilla-7b-hf", "name": "Gorilla 7B Hugging Face", "maker": "berkeley", "family": "berkeley/gorilla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3865, "dossier_id": "berkeley/gorilla/gorilla-7b-tf", "name": "Gorilla 7B TensorFlow Hub", "maker": "berkeley", "family": "berkeley/gorilla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3866, "dossier_id": "berkeley/gorilla/gorilla-7b-th", "name": "Gorilla 7B Torch Hub", "maker": "berkeley", "family": "berkeley/gorilla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3867, "dossier_id": "berkeley/gorilla/gorilla-falcon-7b", "name": "Gorilla Falcon 7B", "maker": "berkeley", "family": "berkeley/gorilla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3868, "dossier_id": "berkeley/gorilla/gorilla-mpt-7b", "name": "Gorilla MPT 7B", "maker": "berkeley", "family": "berkeley/gorilla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3869, "dossier_id": "berkeley/gorilla/gorilla-openfunctions-v0", "name": "Gorilla OpenFunctions v0", "maker": "berkeley", "family": "berkeley/gorilla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3870, "dossier_id": "berkeley/koala/koala-13b", "name": "Koala 13B", "maker": "berkeley", "family": "berkeley/koala", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3871, "dossier_id": "berkeley/koala/koala-7b", "name": "Koala 7B", "maker": "berkeley", "family": "berkeley/koala", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3872, "dossier_id": "berkeley/lwm/lwm-chat", "name": "LWM-Chat", "maker": "berkeley", "family": "berkeley/lwm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3873, "dossier_id": "berkeley/octo/octo-base", "name": "Octo Base", "maker": "berkeley", "family": "berkeley/octo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3874, "dossier_id": "berkeley/octo/octo-small", "name": "Octo Small", "maker": "berkeley", "family": "berkeley/octo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3875, "dossier_id": "berkeley/pillar-0/pillar-0-abdomen-ct", "name": "Pillar-0 Abdomen CT", "maker": "berkeley", "family": "berkeley/pillar-0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3876, "dossier_id": "berkeley/pillar-0/pillar-0-breast-mri", "name": "Pillar-0 Breast MRI", "maker": "berkeley", "family": "berkeley/pillar-0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3877, "dossier_id": "berkeley/pillar-0/pillar-0-chest-ct", "name": "Pillar-0 Chest CT", "maker": "berkeley", "family": "berkeley/pillar-0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3878, "dossier_id": "berkeley/pillar-0/pillar-0-head-ct", "name": "Pillar-0 Head CT", "maker": "berkeley", "family": "berkeley/pillar-0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3879, "dossier_id": "berkeley/starling/starling-rm-34b", "name": "Starling-RM-34B", "maker": "berkeley", "family": "berkeley/starling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3880, "dossier_id": "berkeley/starling/starling-rm-7b-alpha", "name": "Starling-RM-7B-alpha", "maker": "berkeley", "family": "berkeley/starling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3881, "dossier_id": "berkeley/xt/xt", "name": "xT", "maker": "berkeley", "family": "berkeley/xt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3882, "dossier_id": "bern/cage/cage", "name": "CAGE", "maker": "bern", "family": "bern/cage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3883, "dossier_id": "bern/comit/comit-b", "name": "COMiT-B", "maker": "bern", "family": "bern/comit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3884, "dossier_id": "bern/comit/comit-l", "name": "COMiT-L", "maker": "bern", "family": "bern/comit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3885, "dossier_id": "bern/comit/comit-xl", "name": "COMiT-XL", "maker": "bern", "family": "bern/comit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3886, "dossier_id": "bern/hgb/hgb-lm", "name": "HGB Historical Language Model", "maker": "bern", "family": "bern/hgb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3887, "dossier_id": "bern/hgb/hgb-ner", "name": "HGB Nested NER", "maker": "bern", "family": "bern/hgb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3888, "dossier_id": "bern/koenigsfelden/koenigsfelden-ner", "name": "K\u00f6nigsfelden NER", "maker": "bern", "family": "bern/koenigsfelden", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3889, "dossier_id": "bern/kraken/kraken-german-xix", "name": "Kraken German XIX", "maker": "bern", "family": "bern/kraken", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3890, "dossier_id": "bern/kraken/kraken-medieval-german", "name": "Kraken Medieval German", "maker": "bern", "family": "bern/kraken", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3891, "dossier_id": "bern/loom-cfm/loom-cfm", "name": "LOOM-CFM", "maker": "bern", "family": "bern/loom-cfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3892, "dossier_id": "bern/luther/gpt2-larger-luther", "name": "GPT-2 larger Luther", "maker": "bern", "family": "bern/luther", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3893, "dossier_id": "bern/luther/luther-xl", "name": "Luther XL", "maker": "bern", "family": "bern/luther", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3894, "dossier_id": "bern/qwen-htr/qwen3-5-4b-german-xix", "name": "Qwen3.5 4B German XIX", "maker": "bern", "family": "bern/qwen-htr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3895, "dossier_id": "bern/qwen-htr/qwen3vl-german-xix", "name": "Qwen3-VL German XIX", "maker": "bern", "family": "bern/qwen-htr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3896, "dossier_id": "bern/qwen-htr/qwen3vl-medieval-german", "name": "Qwen3-VL Medieval German", "maker": "bern", "family": "bern/qwen-htr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3897, "dossier_id": "bern/river/river", "name": "RIVER", "maker": "bern", "family": "bern/river", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3898, "dossier_id": "bern/trocr/trocr-essoins-middle-latin", "name": "TrOCR Essoins Middle Latin", "maker": "bern", "family": "bern/trocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3899, "dossier_id": "bern/trocr/trocr-kurrent", "name": "TrOCR Kurrent 19th century", "maker": "bern", "family": "bern/trocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3900, "dossier_id": "bern/trocr/trocr-kurrent-xvi-xvii", "name": "TrOCR Kurrent 16th to 18th century", "maker": "bern", "family": "bern/trocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3901, "dossier_id": "bern/trocr/trocr-medieval-escriptmask", "name": "TrOCR Medieval eScriptorium", "maker": "bern", "family": "bern/trocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3902, "dossier_id": "bern/trocr/trocr-towerbooks", "name": "TrOCR Towerbooks", "maker": "bern", "family": "bern/trocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3903, "dossier_id": "bern/turmbuecher/turmbuecher-lm", "name": "Turmb\u00fccher Language Model", "maker": "bern", "family": "bern/turmbuecher", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3904, "dossier_id": "bern/turmbuecher/turmbuecher-ner", "name": "Turmb\u00fccher NER", "maker": "bern", "family": "bern/turmbuecher", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3905, "dossier_id": "bern/walser/gpt2-larger-walser", "name": "GPT-2 larger Walser", "maker": "bern", "family": "bern/walser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3906, "dossier_id": "bern/yoda/yoda", "name": "YODA", "maker": "bern", "family": "bern/yoda", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3907, "dossier_id": "bgu/vanpy/vanpy-age", "name": "VANPY Age Estimator", "maker": "bgu", "family": "bgu/vanpy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3908, "dossier_id": "bgu/vanpy/vanpy-emotion", "name": "VANPY Emotion Classifier", "maker": "bgu", "family": "bgu/vanpy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3909, "dossier_id": "bgu/vanpy/vanpy-gender", "name": "VANPY Gender Classifier", "maker": "bgu", "family": "bgu/vanpy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3910, "dossier_id": "bgu/vanpy/vanpy-height", "name": "VANPY Height Estimator", "maker": "bgu", "family": "bgu/vanpy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3911, "dossier_id": "bigscience/mt0/mt0-base", "name": "mT0-Base", "maker": "bigscience", "family": "bigscience/mt0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3912, "dossier_id": "bigscience/mt0/mt0-large", "name": "mT0-Large", "maker": "bigscience", "family": "bigscience/mt0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3913, "dossier_id": "bigscience/mt0/mt0-small", "name": "mT0-Small", "maker": "bigscience", "family": "bigscience/mt0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3914, "dossier_id": "bigscience/mt0/mt0-xl", "name": "mT0-XL", "maker": "bigscience", "family": "bigscience/mt0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3915, "dossier_id": "bigscience/mt0/mt0-xxl", "name": "mT0-XXL", "maker": "bigscience", "family": "bigscience/mt0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3916, "dossier_id": "bigscience/mt0/mt0-xxl-mt", "name": "mT0-XXL-MT", "maker": "bigscience", "family": "bigscience/mt0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3917, "dossier_id": "bigscience/mt0/mt0-xxl-p3", "name": "mT0-XXL-P3", "maker": "bigscience", "family": "bigscience/mt0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3918, "dossier_id": "bigscience/octopack/octogeex", "name": "OctoGeeX", "maker": "bigscience", "family": "bigscience/octopack", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following code generation. The card says to wrap prompts as Question and Answer. It lists 100+ programming languages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3919, "dossier_id": "bigscience/starpii/starpii", "name": "StarPII", "maker": "bigscience", "family": "bigscience/starpii", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3920, "dossier_id": "bigscience/t0/t0", "name": "T0", "maker": "bigscience", "family": "bigscience/t0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3921, "dossier_id": "bigscience/t0/t0-3b", "name": "T0 3B", "maker": "bigscience", "family": "bigscience/t0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3922, "dossier_id": "bigscience/t0/t0p", "name": "T0p", "maker": "bigscience", "family": "bigscience/t0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3923, "dossier_id": "bigscience/t0/t0pp", "name": "T0pp", "maker": "bigscience", "family": "bigscience/t0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3924, "dossier_id": "birmingham/fourier-net/fourier-net", "name": "Fourier-Net", "maker": "birmingham", "family": "birmingham/fourier-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3925, "dossier_id": "birmingham/fourier-net/fourier-net-plus", "name": "Fourier-Net+", "maker": "birmingham", "family": "birmingham/fourier-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3926, "dossier_id": "birmingham/lessnet/lessnet", "name": "LessNet", "maker": "birmingham", "family": "birmingham/lessnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3927, "dossier_id": "birmingham/lku-net/lku-net", "name": "LKU-Net", "maker": "birmingham", "family": "birmingham/lku-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3928, "dossier_id": "birmingham/mpbppi/mpbppi", "name": "MpbPPI", "maker": "birmingham", "family": "birmingham/mpbppi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3929, "dossier_id": "birmingham/sacb-net/sacb-net", "name": "SACB-Net", "maker": "birmingham", "family": "birmingham/sacb-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3930, "dossier_id": "bit/earthgpt/earthgpt", "name": "EarthGPT", "maker": "bit", "family": "bit/earthgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3931, "dossier_id": "bit/earthgpt/earthgpt-x", "name": "EarthGPT-X", "maker": "bit", "family": "bit/earthgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3932, "dossier_id": "bit/earthgpt/earthmarker", "name": "EarthMarker", "maker": "bit", "family": "bit/earthgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3933, "dossier_id": "bit/popeye/popeye", "name": "Popeye", "maker": "bit", "family": "bit/popeye", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3934, "dossier_id": "bit/rgmt/extreme-rgmt", "name": "Extreme-RGMT", "maker": "bit", "family": "bit/rgmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3935, "dossier_id": "bit/rgmt/longyu", "name": "\u9f99\u9a6d (RGMT)", "maker": "bit", "family": "bit/rgmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3936, "dossier_id": "bjtu/transgpt/transgpt-mm-6b", "name": "TransGPT-MM-6B", "maker": "bjtu", "family": "bjtu/transgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3937, "dossier_id": "bjtu/transgpt/transgpt-sm", "name": "TransGPT-SM", "maker": "bjtu", "family": "bjtu/transgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3938, "dossier_id": "bjtu/videoworld/videoworld", "name": "VideoWorld", "maker": "bjtu", "family": "bjtu/videoworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3939, "dossier_id": "bjtu/videoworld/videoworld-2", "name": "VideoWorld 2", "maker": "bjtu", "family": "bjtu/videoworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3940, "dossier_id": "black-forest-labs/flux-1/flux-1-1-pro", "name": "FLUX1.1 [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3941, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.06, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3942, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3943, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3944, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3945, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3946, "dossier_id": "black-forest-labs/flux-1/flux-1-dev", "name": "FLUX.1 [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.025, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3947, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3948, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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].", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3949, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3950, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3951, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3952, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3953, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3954, "dossier_id": "black-forest-labs/flux-1/flux-1-pro", "name": "FLUX.1 [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3955, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3956, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.06, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3957, "dossier_id": "black-forest-labs/flux-1/flux-1-schnell", "name": "FLUX.1 [schnell]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3958, "dossier_id": "black-forest-labs/flux-2/flux-2-dev", "name": "FLUX.2 [dev]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3959, "dossier_id": "black-forest-labs/flux-2/flux-2-flex", "name": "FLUX.2 [flex]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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].", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3960, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.014, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3961, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.015, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3962, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3963, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3964, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3965, "dossier_id": "black-forest-labs/flux-2/flux-2-max", "name": "FLUX.2 [max]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.07, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3966, "dossier_id": "black-forest-labs/flux-2/flux-2-pro", "name": "FLUX.2 [pro]", "maker": "black-forest-labs", "family": "black-forest-labs/flux-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3967, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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].", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3968, "dossier_id": "black-forest-labs/flux-3/flux-3-action", "name": "FLUX 3 Action", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3969, "dossier_id": "black-forest-labs/flux-3/flux-3-image", "name": "FLUX 3 Image", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.048, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3970, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.009, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3971, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3972, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3973, "dossier_id": "black-forest-labs/flux-3/flux-3-video", "name": "FLUX 3 Video", "maker": "black-forest-labs", "family": "black-forest-labs/flux-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.15, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3974, "dossier_id": "black-forest-labs/flux-tools/flux-deblur", "name": "FLUX Deblur", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3975, "dossier_id": "black-forest-labs/flux-tools/flux-erase", "name": "FLUX Erase", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3976, "dossier_id": "black-forest-labs/flux-tools/flux-outpainting", "name": "FLUX Outpainting", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.1, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3977, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3978, "dossier_id": "black-forest-labs/flux-tools/flux-video-upscale", "name": "FLUX Video Upscale", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.07, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3979, "dossier_id": "black-forest-labs/flux-tools/flux-vto", "name": "FLUX Virtual Try-On", "maker": "black-forest-labs", "family": "black-forest-labs/flux-tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.0375, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3980, "dossier_id": "bnu/taiyan/ai-taiyan", "name": "AI\u592a\u708e", "maker": "bnu", "family": "bnu/taiyan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3981, "dossier_id": "bonn/rivermamba/rivermamba", "name": "RiverMamba", "maker": "bonn", "family": "bonn/rivermamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3982, "dossier_id": "bordeaux/volbrain/assemblynet", "name": "AssemblyNet", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3983, "dossier_id": "bordeaux/volbrain/assemblynet-ad", "name": "AssemblyNet-AD", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3984, "dossier_id": "bordeaux/volbrain/assemblynet-ad-ftd", "name": "AssemblyNet-AD-FTD", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3985, "dossier_id": "bordeaux/volbrain/brain-structure-ages", "name": "BrainStructureAges", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3986, "dossier_id": "bordeaux/volbrain/cognitive-tree", "name": "cognitiveTree", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3987, "dossier_id": "bordeaux/volbrain/deep-lesion-brain", "name": "DeepLesionBrain", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3988, "dossier_id": "bordeaux/volbrain/deep-thalamus", "name": "DeepThalamus", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3989, "dossier_id": "bordeaux/volbrain/deepceres", "name": "DeepCERES", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3990, "dossier_id": "bordeaux/volbrain/motor-tree", "name": "motorTree", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3991, "dossier_id": "bordeaux/volbrain/petbrain", "name": "petBrain", "maker": "bordeaux", "family": "bordeaux/volbrain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3992, "dossier_id": "brandeis/acs/brandeis-acs", "name": "Brandeis Acoustic Classification & Segmentation", "maker": "brandeis", "family": "brandeis/acs", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3993, "dossier_id": "brandeis/transpeaknet/transpeaknet", "name": "TransPeakNet", "maker": "brandeis", "family": "brandeis/transpeaknet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3994, "dossier_id": "bristol/auditory-slow-fast/auditory-slow-fast", "name": "Auditory Slow-Fast", "maker": "bristol", "family": "bristol/auditory-slow-fast", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3995, "dossier_id": "bristol/mtcn/mtcn", "name": "Multimodal Temporal Context Network (MTCN)", "maker": "bristol", "family": "bristol/mtcn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3996, "dossier_id": "bristol/tbn/tbn", "name": "Temporal Binding Network (TBN)", "maker": "bristol", "family": "bristol/tbn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3997, "dossier_id": "brown/ganimorph/ganimorph", "name": "GANimorph", "maker": "brown", "family": "brown/ganimorph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3998, "dossier_id": "brown/opengpt-2/opengpt-2", "name": "OpenGPT-2", "maker": "brown", "family": "brown/opengpt-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 3999, "dossier_id": "brown/r3gan/r3gan", "name": "R3GAN", "maker": "brown", "family": "brown/r3gan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4000, "dossier_id": "bu/mammo/mammo-clip", "name": "Mammo-CLIP", "maker": "bu", "family": "bu/mammo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4001, "dossier_id": "bu/mammo/mammo-fm", "name": "Mammo-FM", "maker": "bu", "family": "bu/mammo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4002, "dossier_id": "bu/medsyn/medsyn", "name": "MedSyn", "maker": "bu", "family": "bu/medsyn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4003, "dossier_id": "bu/medsyn/medsyn2", "name": "MedSyn2", "maker": "bu", "family": "bu/medsyn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4004, "dossier_id": "buaa/tinyllava/tinyllava-1-4b", "name": "TinyLLaVA-1.4B", "maker": "buaa", "family": "buaa/tinyllava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4005, "dossier_id": "buaa/xiaoan/xiaoan", "name": "XiaoAn", "maker": "buaa", "family": "buaa/xiaoan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4006, "dossier_id": "buaa/xiaohang/xiaohang", "name": "XiaoHang", "maker": "buaa", "family": "buaa/xiaohang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4007, "dossier_id": "buffalo/lipinc/lipinc", "name": "LIPINC", "maker": "buffalo", "family": "buffalo/lipinc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4008, "dossier_id": "buffalo/rawnet2-vocoder/rawnet2-vocoder", "name": "RawNet2-Vocoder", "maker": "buffalo", "family": "buffalo/rawnet2-vocoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4009, "dossier_id": "bupt/byte/byte", "name": "\u6bd4\u7279Byte\u8ba1\u7b97\u673a\u5b66\u79d1\u5927\u6a21\u578b", "maker": "bupt", "family": "bupt/byte", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4010, "dossier_id": "bupt/uishift/uishift-7b-2k", "name": "UIShift-7B-2K", "maker": "bupt", "family": "bupt/uishift", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4011, "dossier_id": "bytedance/bagel/bagel", "name": "BAGEL", "maker": "bytedance", "family": "bytedance/bagel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4012, "dossier_id": "bytedance/depth-anything/depth-anything-3", "name": "Depth Anything 3", "maker": "bytedance", "family": "bytedance/depth-anything", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4013, "dossier_id": "bytedance/doubao/doubao-character", "name": "Doubao Character", "maker": "bytedance", "family": "bytedance/doubao", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4014, "dossier_id": "bytedance/doubao/doubao-embedding", "name": "Doubao Embedding", "maker": "bytedance", "family": "bytedance/doubao", "context_length": null, "prompt_price_per_m": 0.7, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4015, "dossier_id": "bytedance/doubao/doubao-lite", "name": "Doubao Lite", "maker": "bytedance", "family": "bytedance/doubao", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4016, "dossier_id": "bytedance/doubao/doubao-pro", "name": "Doubao Pro", "maker": "bytedance", "family": "bytedance/doubao", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4017, "dossier_id": "bytedance/doubao/doubao-vision", "name": "Doubao Vision", "maker": "bytedance", "family": "bytedance/doubao", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4018, "dossier_id": "bytedance/seaweed/seaweed-7b", "name": "Seaweed-7B", "maker": "bytedance", "family": "bytedance/seaweed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4019, "dossier_id": "bytedance/seed/seed-1-5-lite", "name": "Seed 1.5 Lite", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4020, "dossier_id": "bytedance/seed/seed-1-5-pro", "name": "Seed 1.5 Pro", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4021, "dossier_id": "bytedance/seed/seed-1-5-vl", "name": "Seed 1.5 VL", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4022, "dossier_id": "bytedance/seed/seed-1-6", "name": "Seed 1.6", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4023, "dossier_id": "bytedance/seed/seed-1-8", "name": "Seed 1.8", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 0.8, "completion_price_per_m": 2.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4024, "dossier_id": "bytedance/seed/seed-2-0-code", "name": "Seed 2.0 Code", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 3.2, "completion_price_per_m": 16.0, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4025, "dossier_id": "bytedance/seed/seed-2-0-lite", "name": "Seed 2.0 Lite", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 0.6, "completion_price_per_m": 3.6, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4026, "dossier_id": "bytedance/seed/seed-2-0-pro", "name": "Seed 2.0 Pro", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 3.2, "completion_price_per_m": 16.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4027, "dossier_id": "bytedance/seed/seed-2-1-lite", "name": "Seed 2.1 Lite", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 0.8, "completion_price_per_m": 2.7, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4028, "dossier_id": "bytedance/seed/seed-2-1-pro", "name": "Seed 2.1 Pro", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 6.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4029, "dossier_id": "bytedance/seed/seed-character", "name": "Seed Character", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 0.8, "completion_price_per_m": 2.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4030, "dossier_id": "bytedance/seed/seed-coder", "name": "Seed-Coder-8B", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Seed-Coder-8B is ByteDance Seed's open code model, with base, instruct, and reasoning checkpoints released in May 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4031, "dossier_id": "bytedance/seed/seed-diffusion", "name": "Seed Diffusion Preview", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4032, "dossier_id": "bytedance/seed/seed-evolving", "name": "Seed Evolving", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 6.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4033, "dossier_id": "bytedance/seed/seed-translation", "name": "Seed Translation", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": 1.2, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4034, "dossier_id": "bytedance/seed/seed-x", "name": "Seed-X", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4035, "dossier_id": "bytedance/seed/stable-diffcoder", "name": "Stable-DiffCoder", "maker": "bytedance", "family": "bytedance/seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4036, "dossier_id": "bytedance/seed3d/seed3d-1-0", "name": "Seed3D 1.0", "maker": "bytedance", "family": "bytedance/seed3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4037, "dossier_id": "bytedance/seed3d/seed3d-2-0", "name": "Seed3D 2.0", "maker": "bytedance", "family": "bytedance/seed3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 2.4, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4038, "dossier_id": "bytedance/seedance/seedance-1-0-lite", "name": "Seedance 1.0 Lite", "maker": "bytedance", "family": "bytedance/seedance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 1.8, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4039, "dossier_id": "bytedance/seedance/seedance-1-0-pro", "name": "Seedance 1.0 Pro", "maker": "bytedance", "family": "bytedance/seedance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 15.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4040, "dossier_id": "bytedance/seedance/seedance-1-0-pro-fast", "name": "Seedance 1.0 Pro Fast", "maker": "bytedance", "family": "bytedance/seedance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 4.2, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4041, "dossier_id": "bytedance/seedance/seedance-1-5-pro", "name": "Seedance 1.5 Pro", "maker": "bytedance", "family": "bytedance/seedance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4042, "dossier_id": "bytedance/seedance/seedance-2-0", "name": "Seedance 2.0", "maker": "bytedance", "family": "bytedance/seedance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4043, "dossier_id": "bytedance/seedance/seedance-2-0-fast", "name": "Seedance 2.0 Fast", "maker": "bytedance", "family": "bytedance/seedance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4044, "dossier_id": "bytedance/seedance/seedance-2-0-mini", "name": "Seedance 2.0 Mini", "maker": "bytedance", "family": "bytedance/seedance", "context_length": null, "prompt_price_per_m": 22.0, "completion_price_per_m": 37.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4045, "dossier_id": "bytedance/seedance/seedance-2-5", "name": "Seedance 2.5", "maker": "bytedance", "family": "bytedance/seedance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4046, "dossier_id": "bytedance/seededit/seededit-3-0", "name": "SeedEdit 3.0", "maker": "bytedance", "family": "bytedance/seededit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4047, "dossier_id": "bytedance/seedream/seedream-2-0", "name": "Seedream 2.0", "maker": "bytedance", "family": "bytedance/seedream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4048, "dossier_id": "bytedance/seedream/seedream-3-0", "name": "Seedream 3.0", "maker": "bytedance", "family": "bytedance/seedream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4049, "dossier_id": "bytedance/seedream/seedream-4-0", "name": "Seedream 4.0", "maker": "bytedance", "family": "bytedance/seedream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.2, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4050, "dossier_id": "bytedance/seedream/seedream-4-5", "name": "Seedream 4.5", "maker": "bytedance", "family": "bytedance/seedream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.25, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4051, "dossier_id": "bytedance/seedream/seedream-5-0-flash", "name": "Seedream 5.0 Flash", "maker": "bytedance", "family": "bytedance/seedream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.12, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4052, "dossier_id": "bytedance/seedream/seedream-5-0-lite", "name": "Seedream 5.0 Lite", "maker": "bytedance", "family": "bytedance/seedream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.22, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4053, "dossier_id": "bytedance/seedream/seedream-5-0-pro", "name": "Seedream 5.0 Pro", "maker": "bytedance", "family": "bytedance/seedream", "context_length": null, "prompt_price_per_m": 0.02, "completion_price_per_m": 0.3, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4054, "dossier_id": "bytedance/seedvr/seedvr", "name": "SeedVR", "maker": "bytedance", "family": "bytedance/seedvr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4055, "dossier_id": "bytedance/speech/doubao-simul-2-0", "name": "Doubao Simultaneous Interpretation 2.0", "maker": "bytedance", "family": "bytedance/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4056, "dossier_id": "bytedance/speech/seed-audio-1-0", "name": "Seed Audio 1.0", "maker": "bytedance", "family": "bytedance/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4057, "dossier_id": "bytedance/speech/seed-icl-1-0", "name": "Seed-ICL 1.0", "maker": "bytedance", "family": "bytedance/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4058, "dossier_id": "bytedance/speech/seed-icl-2-0", "name": "Seed-ICL 2.0", "maker": "bytedance", "family": "bytedance/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4059, "dossier_id": "bytedance/speech/seed-realtime", "name": "SeedRealtime", "maker": "bytedance", "family": "bytedance/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4060, "dossier_id": "bytedance/speech/seed-tts-1-0", "name": "Seed-TTS 1.0", "maker": "bytedance", "family": "bytedance/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4061, "dossier_id": "bytedance/speech/seed-tts-2-0", "name": "Seed-TTS 2.0", "maker": "bytedance", "family": "bytedance/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4062, "dossier_id": "bytedance/speech/seeduplex", "name": "Seeduplex", "maker": "bytedance", "family": "bytedance/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4063, "dossier_id": "calgary/wavesfm/wavesfm-v1", "name": "WavesFM v1.0", "maker": "calgary", "family": "calgary/wavesfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4064, "dossier_id": "caltech/fno/fno", "name": "Fourier Neural Operator", "maker": "caltech", "family": "caltech/fno", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4065, "dossier_id": "caltech/fno/geo-fno", "name": "Geo-FNO", "maker": "caltech", "family": "caltech/fno", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4066, "dossier_id": "caltech/fno/pino", "name": "Physics-Informed Neural Operator", "maker": "caltech", "family": "caltech/fno", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4067, "dossier_id": "caltech/gano/cgm-gano", "name": "cGM-GANO", "maker": "caltech", "family": "caltech/gano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4068, "dossier_id": "caltech/gano/gano", "name": "GANO", "maker": "caltech", "family": "caltech/gano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4069, "dossier_id": "caltech/neural-fly/neural-fly", "name": "Neural-Fly", "maker": "caltech", "family": "caltech/neural-fly", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4070, "dossier_id": "caltech/neural-lander/neural-lander", "name": "Neural Lander", "maker": "caltech", "family": "caltech/neural-lander", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4071, "dossier_id": "caltech/orbnet/orbnet", "name": "OrbNet", "maker": "caltech", "family": "caltech/orbnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4072, "dossier_id": "caltech/orbnet/orbnet-equi", "name": "OrbNet-Equi", "maker": "caltech", "family": "caltech/orbnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4073, "dossier_id": "cambridge/bioredditbert/bioredditbert", "name": "BioRedditBERT", "maker": "cambridge", "family": "cambridge/bioredditbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4074, "dossier_id": "cambridge/mace/mace-ani-cc", "name": "MACE-ANI-CC", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4075, "dossier_id": "cambridge/mace/mace-matpes-pbe-0", "name": "MACE-MATPES-PBE-0", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4076, "dossier_id": "cambridge/mace/mace-matpes-r2scan-0", "name": "MACE-MATPES-r2SCAN-0", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4077, "dossier_id": "cambridge/mace/mace-mdp", "name": "MACE-MDP", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4078, "dossier_id": "cambridge/mace/mace-mh-0", "name": "MACE-MH-0", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4079, "dossier_id": "cambridge/mace/mace-mh-1", "name": "MACE-MH-1", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4080, "dossier_id": "cambridge/mace/mace-mp-0", "name": "MACE-MP-0", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4081, "dossier_id": "cambridge/mace/mace-mpa-0", "name": "MACE-MPA-0", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4082, "dossier_id": "cambridge/mace/mace-off23", "name": "MACE-OFF23", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4083, "dossier_id": "cambridge/mace/mace-off24", "name": "MACE-OFF24", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4084, "dossier_id": "cambridge/mace/mace-omat-0", "name": "MACE-OMAT-0", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4085, "dossier_id": "cambridge/mace/mace-omol-0", "name": "MACE-OMOL-0", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4086, "dossier_id": "cambridge/mace/mace-polar-1", "name": "MACE-POLAR-1", "maker": "cambridge", "family": "cambridge/mace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4087, "dossier_id": "cambridge/mirror-bert/mirror-bert", "name": "Mirror-BERT", "maker": "cambridge", "family": "cambridge/mirror-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4088, "dossier_id": "cambridge/mirror-bert/mirror-roberta", "name": "Mirror-RoBERTa", "maker": "cambridge", "family": "cambridge/mirror-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4089, "dossier_id": "cambridge/mirrorwic/mirrorwic", "name": "MirrorWiC", "maker": "cambridge", "family": "cambridge/mirrorwic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4090, "dossier_id": "cambridge/posenet/posenet", "name": "PoseNet (Cambridge relocalization)", "maker": "cambridge", "family": "cambridge/posenet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4091, "dossier_id": "cambridge/sapbert/sapbert-pubmedbert", "name": "SapBERT (PubMedBERT)", "maker": "cambridge", "family": "cambridge/sapbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4092, "dossier_id": "cambridge/sapbert/sapbert-xlmr", "name": "SapBERT-XLMR", "maker": "cambridge", "family": "cambridge/sapbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4093, "dossier_id": "cambridge/sapbert/sapbert-xlmr-large", "name": "SapBERT-XLMR-large", "maker": "cambridge", "family": "cambridge/sapbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4094, "dossier_id": "cambridge/segnet/bayesian-segnet", "name": "Bayesian SegNet", "maker": "cambridge", "family": "cambridge/segnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4095, "dossier_id": "cambridge/segnet/segnet", "name": "SegNet", "maker": "cambridge", "family": "cambridge/segnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4096, "dossier_id": "cambridge/simctg/simctg", "name": "SimCTG", "maker": "cambridge", "family": "cambridge/simctg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4097, "dossier_id": "cambridge/tessera/tessera-v1-0", "name": "TESSERA v1.0", "maker": "cambridge", "family": "cambridge/tessera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4098, "dossier_id": "cambridge/tessera/tessera-v1-1", "name": "TESSERA v1.1", "maker": "cambridge", "family": "cambridge/tessera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4099, "dossier_id": "cambridge/tessera/tessera-v2-large", "name": "TESSERA v2 Large", "maker": "cambridge", "family": "cambridge/tessera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4100, "dossier_id": "cambridge/tessera/tessera-v2-medium", "name": "TESSERA v2 Medium", "maker": "cambridge", "family": "cambridge/tessera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4101, "dossier_id": "cambridge/tessera/tessera-v2-nano", "name": "TESSERA v2 Nano", "maker": "cambridge", "family": "cambridge/tessera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4102, "dossier_id": "cambridge/tessera/tessera-v2-small", "name": "TESSERA v2 Small", "maker": "cambridge", "family": "cambridge/tessera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4103, "dossier_id": "cambridge/tessera/tessera-v2-teacher", "name": "TESSERA v2 2B teacher", "maker": "cambridge", "family": "cambridge/tessera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4104, "dossier_id": "captions/mirage/mirage-avatar-x", "name": "Mirage Avatar X", "maker": "captions", "family": "captions/mirage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.15, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4105, "dossier_id": "captions/mirage/mirage-video-1", "name": "Mirage Video 1", "maker": "captions", "family": "captions/mirage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.175, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4106, "dossier_id": "cardiff/sensitive/sensitive-binary", "name": "X-Sensitive binary", "maker": "cardiff", "family": "cardiff/sensitive", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4107, "dossier_id": "cardiff/sensitive/sensitive-multilabel", "name": "X-Sensitive multilabel", "maker": "cardiff", "family": "cardiff/sensitive", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4108, "dossier_id": "cardiff/timelm/timelm-base", "name": "TimeLM RoBERTa-base", "maker": "cardiff", "family": "cardiff/timelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4109, "dossier_id": "cardiff/timelm/timelm-large", "name": "TimeLM RoBERTa-large", "maker": "cardiff", "family": "cardiff/timelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4110, "dossier_id": "cardiff/tweetnlp/emoji", "name": "Twitter-RoBERTa emoji", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4111, "dossier_id": "cardiff/tweetnlp/emotion", "name": "Twitter-RoBERTa emotion", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4112, "dossier_id": "cardiff/tweetnlp/emotion-multilabel", "name": "Twitter-RoBERTa emotion (multilabel)", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4113, "dossier_id": "cardiff/tweetnlp/hate", "name": "Twitter-RoBERTa hate", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4114, "dossier_id": "cardiff/tweetnlp/hate-multiclass", "name": "Twitter-RoBERTa hate (multiclass)", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4115, "dossier_id": "cardiff/tweetnlp/irony", "name": "Twitter-RoBERTa irony", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4116, "dossier_id": "cardiff/tweetnlp/offensive", "name": "Twitter-RoBERTa offensive", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4117, "dossier_id": "cardiff/tweetnlp/sentiment", "name": "Twitter-RoBERTa sentiment", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4118, "dossier_id": "cardiff/tweetnlp/stance", "name": "Twitter-RoBERTa stance", "maker": "cardiff", "family": "cardiff/tweetnlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4119, "dossier_id": "cardiff/tweettopic/tweet-topic-multi", "name": "TweetTopic multi", "maker": "cardiff", "family": "cardiff/tweettopic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4120, "dossier_id": "cardiff/tweettopic/tweet-topic-multilingual-base", "name": "TweetTopic multilingual base", "maker": "cardiff", "family": "cardiff/tweettopic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4121, "dossier_id": "cardiff/tweettopic/tweet-topic-multilingual-large", "name": "TweetTopic multilingual large", "maker": "cardiff", "family": "cardiff/tweettopic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4122, "dossier_id": "cardiff/tweettopic/tweet-topic-single", "name": "TweetTopic single", "maker": "cardiff", "family": "cardiff/tweettopic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4123, "dossier_id": "cardiff/twitter-roberta/twitter-roberta-base", "name": "Twitter-RoBERTa-base", "maker": "cardiff", "family": "cardiff/twitter-roberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4124, "dossier_id": "cardiff/twitter-roberta/twitter-scratch-roberta-base", "name": "Twitter-RoBERTa-base (from scratch)", "maker": "cardiff", "family": "cardiff/twitter-roberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4125, "dossier_id": "cardiff/xlmt/xlmt-base", "name": "XLM-T base", "maker": "cardiff", "family": "cardiff/xlmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4126, "dossier_id": "cardiff/xlmt/xlmt-large", "name": "XLM-T large", "maker": "cardiff", "family": "cardiff/xlmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4127, "dossier_id": "cardiff/xlmt/xlmt-sentiment", "name": "XLM-T sentiment", "maker": "cardiff", "family": "cardiff/xlmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4128, "dossier_id": "cardiff/xlmt/xlmt-sentiment-politics", "name": "XLM-T sentiment (politics)", "maker": "cardiff", "family": "cardiff/xlmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4129, "dossier_id": "cartesia/ink/ink-2", "name": "Ink 2", "maker": "cartesia", "family": "cartesia/ink", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4130, "dossier_id": "cartesia/ink/ink-preview", "name": "Ink Preview", "maker": "cartesia", "family": "cartesia/ink", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4131, "dossier_id": "cartesia/ink/ink-whisper", "name": "Ink Whisper", "maker": "cartesia", "family": "cartesia/ink", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4132, "dossier_id": "cartesia/sonic/sonic", "name": "Sonic", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4133, "dossier_id": "cartesia/sonic/sonic-2", "name": "Sonic 2", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4134, "dossier_id": "cartesia/sonic/sonic-3", "name": "Sonic 3", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cartesia describes high naturalness, accurate transcript following, and low latency for real-time conversation, with fine-grained delivery controls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4135, "dossier_id": "cartesia/sonic/sonic-3-5", "name": "Sonic 3.5", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4136, "dossier_id": "cartesia/sonic/sonic-3-6", "name": "Sonic 3.6", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4137, "dossier_id": "cartesia/sonic/sonic-english", "name": "Sonic English", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4138, "dossier_id": "cartesia/sonic/sonic-multilingual", "name": "Sonic Multilingual", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4139, "dossier_id": "cartesia/sonic/sonic-preview", "name": "Sonic Preview", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4140, "dossier_id": "cartesia/sonic/sonic-turbo", "name": "Sonic Turbo", "maker": "cartesia", "family": "cartesia/sonic", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4141, "dossier_id": "central-south/csu-ms2/csu-ms2", "name": "CSU-MS2", "maker": "central-south", "family": "central-south/csu-ms2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4142, "dossier_id": "central-south/flowinone/flowinone", "name": "FlowInOne", "maker": "central-south", "family": "central-south/flowinone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4143, "dossier_id": "central-south/glance/glance", "name": "Glance", "maker": "central-south", "family": "central-south/glance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4144, "dossier_id": "central-south/glance/glance-forcing", "name": "Glance Forcing", "maker": "central-south", "family": "central-south/glance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4145, "dossier_id": "central-south/rda/rda-llamagen", "name": "RDA LlamaGen", "maker": "central-south", "family": "central-south/rda", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4146, "dossier_id": "chalmers/dlkcat/dlkcat", "name": "DLKcat", "maker": "chalmers", "family": "chalmers/dlkcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4147, "dossier_id": "chalmers/microvision/microvision-fasterrcnn", "name": "MicroVision Faster R-CNN", "maker": "chalmers", "family": "chalmers/microvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4148, "dossier_id": "chalmers/microvision/microvision-rfdetr", "name": "MicroVision RF-DETR", "maker": "chalmers", "family": "chalmers/microvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4149, "dossier_id": "chalmers/microvision/microvision-yolo11", "name": "MicroVision YOLO11", "maker": "chalmers", "family": "chalmers/microvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4150, "dossier_id": "character-ai/c1/c1-1", "name": "C1.1", "maker": "character-ai", "family": "character-ai/c1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4151, "dossier_id": "character-ai/c1/c1-2", "name": "C1.2", "maker": "character-ai", "family": "character-ai/c1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4152, "dossier_id": "character-ai/cai-image/cai-image", "name": "CAI-Image", "maker": "character-ai", "family": "character-ai/cai-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4153, "dossier_id": "character-ai/cai-image/cai-image-v2", "name": "CAI-Image V2", "maker": "character-ai", "family": "character-ai/cai-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4154, "dossier_id": "character-ai/chat/deepsqueak", "name": "DeepSqueak", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4155, "dossier_id": "character-ai/chat/deepsqueak-2", "name": "DeepSqueak 2", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4156, "dossier_id": "character-ai/chat/dynamic", "name": "Dynamic", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Help-center copy: a newest chat style that picks the best option for the conversation, rather than one fixed behavior.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4157, "dossier_id": "character-ai/chat/flash", "name": "Flash", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The January 22, 2025 community update describes Flash as the quicker-responses option next to Prime and Braniac.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4158, "dossier_id": "character-ai/chat/goro", "name": "Goro", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4159, "dossier_id": "character-ai/chat/longsqueak", "name": "LongSqueak", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4160, "dossier_id": "character-ai/chat/meow", "name": "Meow", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4161, "dossier_id": "character-ai/chat/nyan", "name": "Nyan", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4162, "dossier_id": "character-ai/chat/pawly", "name": "Pawly", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The help-center update says Pawly is a model with smarter multilingual capabilities. It does not list the languages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4163, "dossier_id": "character-ai/chat/pipsqueak", "name": "PipSqueak", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4164, "dossier_id": "character-ai/chat/pipsqueak-2", "name": "PipSqueak 2", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4165, "dossier_id": "character-ai/chat/pipsqueak-2-rawr", "name": "PipSqueak 2 Rawr", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4166, "dossier_id": "character-ai/chat/pipsqueak-3", "name": "PipSqueak 3", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4167, "dossier_id": "character-ai/chat/prime", "name": "Prime", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4168, "dossier_id": "character-ai/chat/roar", "name": "Roar", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Launch copy calls Roar a blend of quick thinking and insightful responses, and a general option for conversation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4169, "dossier_id": "character-ai/chat/shortsqueak", "name": "ShortSqueak", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4170, "dossier_id": "character-ai/chat/soft-launch", "name": "Soft Launch", "maker": "character-ai", "family": "character-ai/chat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4171, "dossier_id": "character-ai/kaiju/kaiju-large", "name": "Kaiju Large", "maker": "character-ai", "family": "character-ai/kaiju", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4172, "dossier_id": "character-ai/kaiju/kaiju-medium", "name": "Kaiju Medium", "maker": "character-ai", "family": "character-ai/kaiju", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4173, "dossier_id": "character-ai/kaiju/kaiju-small", "name": "Kaiju Small", "maker": "character-ai", "family": "character-ai/kaiju", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4174, "dossier_id": "character-ai/ovi/ovi", "name": "Ovi", "maker": "character-ai", "family": "character-ai/ovi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4175, "dossier_id": "character-ai/video/avatarfx", "name": "AvatarFX", "maker": "character-ai", "family": "character-ai/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4176, "dossier_id": "character-ai/video/talkingmachines", "name": "TalkingMachines", "maker": "character-ai", "family": "character-ai/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4177, "dossier_id": "cmu/mamba/mamba-1-4b", "name": "Mamba 1.4B", "maker": "cmu", "family": "cmu/mamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4178, "dossier_id": "cmu/mamba/mamba-130m", "name": "Mamba 130M", "maker": "cmu", "family": "cmu/mamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4179, "dossier_id": "cmu/mamba/mamba-2-8b", "name": "Mamba 2.8B", "maker": "cmu", "family": "cmu/mamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4180, "dossier_id": "cmu/mamba/mamba-2-8b-slimpj", "name": "Mamba 2.8B SlimPajama", "maker": "cmu", "family": "cmu/mamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4181, "dossier_id": "cmu/mamba/mamba-370m", "name": "Mamba 370M", "maker": "cmu", "family": "cmu/mamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4182, "dossier_id": "cmu/mamba/mamba-790m", "name": "Mamba 790M", "maker": "cmu", "family": "cmu/mamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4183, "dossier_id": "cmu/mamba-2/mamba2-1-3b", "name": "Mamba-2 1.3B", "maker": "cmu", "family": "cmu/mamba-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4184, "dossier_id": "cmu/mamba-2/mamba2-130m", "name": "Mamba-2 130M", "maker": "cmu", "family": "cmu/mamba-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4185, "dossier_id": "cmu/mamba-2/mamba2-2-7b", "name": "Mamba-2 2.7B", "maker": "cmu", "family": "cmu/mamba-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4186, "dossier_id": "cmu/mamba-2/mamba2-370m", "name": "Mamba-2 370M", "maker": "cmu", "family": "cmu/mamba-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4187, "dossier_id": "cmu/mamba-2/mamba2-780m", "name": "Mamba-2 780M", "maker": "cmu", "family": "cmu/mamba-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4188, "dossier_id": "cmu/openbeats/openbeats-base", "name": "OpenBEATs-Base", "maker": "cmu", "family": "cmu/openbeats", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4189, "dossier_id": "cmu/openbeats/openbeats-large", "name": "OpenBEATs-Large", "maker": "cmu", "family": "cmu/openbeats", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4190, "dossier_id": "cmu/opuslm/opuslm-1-7b-anneal", "name": "OpusLM 1.7B Anneal", "maker": "cmu", "family": "cmu/opuslm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4191, "dossier_id": "cmu/opuslm/opuslm-7b-anneal", "name": "OpusLM 7B Anneal", "maker": "cmu", "family": "cmu/opuslm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4192, "dossier_id": "cmu/owls/owls-0-25b-180k", "name": "OWLS 0.25B 180K", "maker": "cmu", "family": "cmu/owls", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4193, "dossier_id": "cmu/owls/owls-0-5b-180k", "name": "OWLS 0.50B 180K", "maker": "cmu", "family": "cmu/owls", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4194, "dossier_id": "cmu/owls/owls-18b-180k", "name": "OWLS 18B 180K", "maker": "cmu", "family": "cmu/owls", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4195, "dossier_id": "cmu/owls/owls-18b-360k", "name": "OWLS 18B 360K", "maker": "cmu", "family": "cmu/owls", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4196, "dossier_id": "cmu/owls/owls-1b-180k", "name": "OWLS 1B 180K", "maker": "cmu", "family": "cmu/owls", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4197, "dossier_id": "cmu/owls/owls-2b-180k", "name": "OWLS 2B 180K", "maker": "cmu", "family": "cmu/owls", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4198, "dossier_id": "cmu/owls/owls-4b-180k", "name": "OWLS 4B 180K", "maker": "cmu", "family": "cmu/owls", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4199, "dossier_id": "cmu/owls/owls-9b-180k", "name": "OWLS 9B 180K", "maker": "cmu", "family": "cmu/owls", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4200, "dossier_id": "cmu/owsm/owsm-ctc-v3-1", "name": "OWSM-CTC v3.1 medium", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4201, "dossier_id": "cmu/owsm/owsm-ctc-v3-2", "name": "OWSM-CTC v3.2 medium", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4202, "dossier_id": "cmu/owsm/owsm-ctc-v4", "name": "OWSM-CTC v4 medium", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4203, "dossier_id": "cmu/owsm/owsm-v1", "name": "OWSM v1", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4204, "dossier_id": "cmu/owsm/owsm-v2", "name": "OWSM v2", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4205, "dossier_id": "cmu/owsm/owsm-v2-ebranchformer", "name": "OWSM v2 E-Branchformer", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4206, "dossier_id": "cmu/owsm/owsm-v3", "name": "OWSM v3", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4207, "dossier_id": "cmu/owsm/owsm-v3-1-base", "name": "OWSM v3.1 base", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4208, "dossier_id": "cmu/owsm/owsm-v3-1-medium", "name": "OWSM v3.1 medium", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4209, "dossier_id": "cmu/owsm/owsm-v3-1-small", "name": "OWSM v3.1 small", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4210, "dossier_id": "cmu/owsm/owsm-v3-1-small-lowrestriction", "name": "OWSM v3.1 small low-restriction", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4211, "dossier_id": "cmu/owsm/owsm-v3-2", "name": "OWSM v3.2 small", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4212, "dossier_id": "cmu/owsm/owsm-v4-base", "name": "OWSM v4 base", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4213, "dossier_id": "cmu/owsm/owsm-v4-medium", "name": "OWSM v4 medium", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4214, "dossier_id": "cmu/owsm/owsm-v4-small", "name": "OWSM v4 small", "maker": "cmu", "family": "cmu/owsm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab's stated job is open Whisper-style speech recognition, translation, and language identification using public data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4215, "dossier_id": "cmu/pangea/culturalpangea-7b", "name": "CulturalPangea-7B", "maker": "cmu", "family": "cmu/pangea", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4216, "dossier_id": "cmu/pangea/pangea-7b", "name": "Pangea-7B", "maker": "cmu", "family": "cmu/pangea", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4217, "dossier_id": "cmu/polycoder/polycoder-160m", "name": "PolyCoder 160M", "maker": "cmu", "family": "cmu/polycoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4218, "dossier_id": "cmu/polycoder/polycoder-400m", "name": "PolyCoder 400M", "maker": "cmu", "family": "cmu/polycoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4219, "dossier_id": "cmu/sphinx/pocketsphinx", "name": "PocketSphinx", "maker": "cmu", "family": "cmu/sphinx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4220, "dossier_id": "cmu/xeus/xeus", "name": "XEUS", "maker": "cmu", "family": "cmu/xeus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "WAVLab presents XEUS as a universal speech representation model for thousands of languages, including low-resource ASR after fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4221, "dossier_id": "cmu/xlnet/xlnet-base-cased", "name": "XLNet-Base (cased)", "maker": "cmu", "family": "cmu/xlnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4222, "dossier_id": "cmu/xlnet/xlnet-large-cased", "name": "XLNet-Large (cased)", "maker": "cmu", "family": "cmu/xlnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4223, "dossier_id": "cnrs/lebenchmark/wav2vec-fr-1k-female-base", "name": "LeBenchmark wav2vec FR 1K female base", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4224, "dossier_id": "cnrs/lebenchmark/wav2vec-fr-1k-female-large", "name": "LeBenchmark wav2vec FR 1K female large", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4225, "dossier_id": "cnrs/lebenchmark/wav2vec-fr-1k-male-base", "name": "LeBenchmark wav2vec FR 1K male base", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4226, "dossier_id": "cnrs/lebenchmark/wav2vec-fr-1k-male-large", "name": "LeBenchmark wav2vec FR 1K male large", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4227, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-14k-large", "name": "LeBenchmark wav2vec2 FR 14K large", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4228, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-14k-light", "name": "LeBenchmark wav2vec2 FR 14K light", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4229, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-14k-xlarge", "name": "LeBenchmark wav2vec2 FR 14K xlarge", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4230, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-1k-base", "name": "LeBenchmark wav2vec2 FR 1K base", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4231, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-1k-large", "name": "LeBenchmark wav2vec2 FR 1K large", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4232, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-2-6k-base", "name": "LeBenchmark wav2vec2 FR 2.6K base", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4233, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-3k-base", "name": "LeBenchmark wav2vec2 FR 3K base", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4234, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-3k-large", "name": "LeBenchmark wav2vec2 FR 3K large", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4235, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-7k-base", "name": "LeBenchmark wav2vec2 FR 7K base", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4236, "dossier_id": "cnrs/lebenchmark/wav2vec2-fr-7k-large", "name": "LeBenchmark wav2vec2 FR 7K large", "maker": "cnrs", "family": "cnrs/lebenchmark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4237, "dossier_id": "cnrs/pantagruel/pantagruel-b-14k", "name": "Pantagruel-B-14k", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4238, "dossier_id": "cnrs/pantagruel/pantagruel-b-1k", "name": "Pantagruel-B-1k", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4239, "dossier_id": "cnrs/pantagruel/pantagruel-b-camtok-osc", "name": "Pantagruel-B-camtok-Osc", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4240, "dossier_id": "cnrs/pantagruel/pantagruel-b-camtok-wk", "name": "Pantagruel-B-camtok-Wk", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4241, "dossier_id": "cnrs/pantagruel/pantagruel-b-crs-mlm", "name": "Pantagruel-B-Crs-MLM", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4242, "dossier_id": "cnrs/pantagruel/pantagruel-b-osc-mlm", "name": "Pantagruel-B-Osc-MLM", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4243, "dossier_id": "cnrs/pantagruel/pantagruel-b-wk", "name": "Pantagruel-B-Wk", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4244, "dossier_id": "cnrs/pantagruel/pantagruel-b-wk-mlm", "name": "Pantagruel-B-Wk-MLM", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4245, "dossier_id": "cnrs/pantagruel/pantagruel-l-114k", "name": "Pantagruel-L-114k", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4246, "dossier_id": "cnrs/pantagruel/pantagruel-l-14k", "name": "Pantagruel-L-14k", "maker": "cnrs", "family": "cnrs/pantagruel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4247, "dossier_id": "cohere/aya/aya-101", "name": "Aya 101", "maker": "cohere", "family": "cohere/aya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4248, "dossier_id": "cohere/aya/aya-23-35b", "name": "Aya 23 35B", "maker": "cohere", "family": "cohere/aya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4249, "dossier_id": "cohere/aya/aya-23-8b", "name": "Aya 23 8B", "maker": "cohere", "family": "cohere/aya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4250, "dossier_id": "cohere/aya/tiny-aya-base", "name": "Tiny Aya Base", "maker": "cohere", "family": "cohere/aya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4251, "dossier_id": "cohere/command/command-r7b-arabic-02-2025", "name": "Command R7B Arabic", "maker": "cohere", "family": "cohere/command", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4252, "dossier_id": "cohere/embed/embed-english-light-v2-0", "name": "Embed English Light v2", "maker": "cohere", "family": "cohere/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed English Light v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4253, "dossier_id": "cohere/embed/embed-english-v2-0", "name": "Embed English v2", "maker": "cohere", "family": "cohere/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed English v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4254, "dossier_id": "cohere/embed/embed-multilingual-v2-0", "name": "Embed Multilingual v2", "maker": "cohere", "family": "cohere/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed Multilingual v2 is a retired Cohere embedding model. API requests using this id failed starting 4 April 2026.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4255, "dossier_id": "cohere/rerank/rerank-english-v2-0", "name": "Rerank English v2", "maker": "cohere", "family": "cohere/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4256, "dossier_id": "cohere/rerank/rerank-multilingual-v2-0", "name": "Rerank Multilingual v2", "maker": "cohere", "family": "cohere/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4257, "dossier_id": "cohere/transcribe/cohere-transcribe-03-2026", "name": "Cohere Transcribe", "maker": "cohere", "family": "cohere/transcribe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4258, "dossier_id": "cohere/transcribe/cohere-transcribe-arabic-07-2026", "name": "Cohere Transcribe Arabic", "maker": "cohere", "family": "cohere/transcribe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4259, "dossier_id": "coimbra/pt-lkb/pt-lkb-embeddings", "name": "PT-LKB embeddings", "maker": "coimbra", "family": "coimbra/pt-lkb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4260, "dossier_id": "cologne/grandqc/grandqc-artifact", "name": "GrandQC Artifact Segmentation", "maker": "cologne", "family": "cologne/grandqc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4261, "dossier_id": "cologne/grandqc/grandqc-tissue", "name": "GrandQC Tissue Detection", "maker": "cologne", "family": "cologne/grandqc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4262, "dossier_id": "cologne/rapidlymphoma/rapidlymphoma", "name": "RapidLymphoma", "maker": "cologne", "family": "cologne/rapidlymphoma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4263, "dossier_id": "colorado/livedst/livedst", "name": "LiveDst", "maker": "colorado", "family": "colorado/livedst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4264, "dossier_id": "colorado/livewire/livewire", "name": "LiveWire", "maker": "colorado", "family": "colorado/livewire", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4265, "dossier_id": "colossyan/avatar/cartoon", "name": "Cartoon avatars", "maker": "colossyan", "family": "colossyan/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4266, "dossier_id": "colossyan/avatar/instant", "name": "Instant avatar", "maker": "colossyan", "family": "colossyan/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4267, "dossier_id": "colossyan/avatar/lite", "name": "Lite avatar", "maker": "colossyan", "family": "colossyan/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4268, "dossier_id": "colossyan/avatar/scenario", "name": "Scenario avatars", "maker": "colossyan", "family": "colossyan/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4269, "dossier_id": "colossyan/avatar/ssa-lite", "name": "ssa_lite", "maker": "colossyan", "family": "colossyan/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4270, "dossier_id": "colossyan/avatar/ssa-studio", "name": "ssa_studio", "maker": "colossyan", "family": "colossyan/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4271, "dossier_id": "colossyan/avatar/studio", "name": "Studio avatars", "maker": "colossyan", "family": "colossyan/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4272, "dossier_id": "colossyan/dubbing/colossyan-dubbing", "name": "Colossyan Dubbing", "maker": "colossyan", "family": "colossyan/dubbing", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4273, "dossier_id": "colossyan/legacy/legacy", "name": "Legacy", "maker": "colossyan", "family": "colossyan/legacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4274, "dossier_id": "colossyan/neo/neo", "name": "NEO", "maker": "colossyan", "family": "colossyan/neo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4275, "dossier_id": "colossyan/neo/neo-1-5", "name": "NEO 1.5", "maker": "colossyan", "family": "colossyan/neo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4276, "dossier_id": "colossyan/neo/neo-2", "name": "NEO 2", "maker": "colossyan", "family": "colossyan/neo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4277, "dossier_id": "colossyan/neo/pictar", "name": "Pictar", "maker": "colossyan", "family": "colossyan/neo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4278, "dossier_id": "colossyan/neo/vocal", "name": "VOCAL", "maker": "colossyan", "family": "colossyan/neo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4279, "dossier_id": "colossyan/omnia/omnia", "name": "Omnia", "maker": "colossyan", "family": "colossyan/omnia", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4280, "dossier_id": "colossyan/stock/alicia", "name": "Alicia", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4281, "dossier_id": "colossyan/stock/amelia", "name": "Amelia", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4282, "dossier_id": "colossyan/stock/angelina", "name": "Angelina", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4283, "dossier_id": "colossyan/stock/anthony", "name": "Anthony", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4284, "dossier_id": "colossyan/stock/arnold", "name": "Arnold", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4285, "dossier_id": "colossyan/stock/ashley", "name": "Ashley", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4286, "dossier_id": "colossyan/stock/barbara", "name": "Barbara", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4287, "dossier_id": "colossyan/stock/caleb", "name": "Caleb", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4288, "dossier_id": "colossyan/stock/camila", "name": "Camila", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4289, "dossier_id": "colossyan/stock/daisy", "name": "Daisy", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4290, "dossier_id": "colossyan/stock/donald", "name": "Donald", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4291, "dossier_id": "colossyan/stock/doug", "name": "Doug", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4292, "dossier_id": "colossyan/stock/dwane", "name": "Dwane", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4293, "dossier_id": "colossyan/stock/dylan", "name": "Dylan", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4294, "dossier_id": "colossyan/stock/emilio", "name": "Emilio", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4295, "dossier_id": "colossyan/stock/gloria", "name": "Gloria", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4296, "dossier_id": "colossyan/stock/hamilton", "name": "Hamilton", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4297, "dossier_id": "colossyan/stock/harry", "name": "Harry", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4298, "dossier_id": "colossyan/stock/henry", "name": "Henry", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4299, "dossier_id": "colossyan/stock/ivy", "name": "Ivy", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4300, "dossier_id": "colossyan/stock/jade", "name": "Jade", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4301, "dossier_id": "colossyan/stock/jason", "name": "Jason", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4302, "dossier_id": "colossyan/stock/jerry", "name": "Jerry", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4303, "dossier_id": "colossyan/stock/jing", "name": "Jing", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4304, "dossier_id": "colossyan/stock/ken", "name": "Ken", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4305, "dossier_id": "colossyan/stock/kevin", "name": "Kevin", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4306, "dossier_id": "colossyan/stock/lara", "name": "Lara", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4307, "dossier_id": "colossyan/stock/laura", "name": "Laura", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4308, "dossier_id": "colossyan/stock/lea", "name": "Lea", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4309, "dossier_id": "colossyan/stock/linda", "name": "Linda", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4310, "dossier_id": "colossyan/stock/lisa", "name": "Lisa", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4311, "dossier_id": "colossyan/stock/louis", "name": "Louis", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4312, "dossier_id": "colossyan/stock/maya", "name": "Maya", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4313, "dossier_id": "colossyan/stock/megan", "name": "Megan", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4314, "dossier_id": "colossyan/stock/michael", "name": "Michael", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4315, "dossier_id": "colossyan/stock/miley", "name": "Miley", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4316, "dossier_id": "colossyan/stock/nada", "name": "Nada", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4317, "dossier_id": "colossyan/stock/natalie", "name": "Natalie", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4318, "dossier_id": "colossyan/stock/nate", "name": "Nate", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4319, "dossier_id": "colossyan/stock/nathan", "name": "Nathan", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4320, "dossier_id": "colossyan/stock/nelly", "name": "Nelly", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4321, "dossier_id": "colossyan/stock/niall", "name": "Niall", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4322, "dossier_id": "colossyan/stock/nina", "name": "Nina", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4323, "dossier_id": "colossyan/stock/oliver", "name": "Oliver", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4324, "dossier_id": "colossyan/stock/omar", "name": "Omar", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4325, "dossier_id": "colossyan/stock/owen", "name": "Owen", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4326, "dossier_id": "colossyan/stock/patricia", "name": "Patricia", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4327, "dossier_id": "colossyan/stock/peter", "name": "Peter", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4328, "dossier_id": "colossyan/stock/riley", "name": "Riley", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4329, "dossier_id": "colossyan/stock/rima", "name": "Rima", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4330, "dossier_id": "colossyan/stock/ryan", "name": "Ryan", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4331, "dossier_id": "colossyan/stock/ton", "name": "Ton", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4332, "dossier_id": "colossyan/stock/yukiko", "name": "Yukiko", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4333, "dossier_id": "colossyan/stock/yusuf", "name": "Yusuf", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4334, "dossier_id": "colossyan/stock/zoe", "name": "Zoe", "maker": "colossyan", "family": "colossyan/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4335, "dossier_id": "columbia/openbind/openbind-0", "name": "OpenBind-0", "maker": "columbia", "family": "columbia/openbind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4336, "dossier_id": "columbia/openfold/openfold", "name": "OpenFold", "maker": "columbia", "family": "columbia/openfold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4337, "dossier_id": "columbia/openfold/openfold-soloseq", "name": "OpenFold-SoloSeq", "maker": "columbia", "family": "columbia/openfold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4338, "dossier_id": "columbia/openfold3/openfold3-preview", "name": "OpenFold3-preview", "maker": "columbia", "family": "columbia/openfold3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4339, "dossier_id": "columbia/openfold3/openfold3-preview2", "name": "OpenFold3-preview2", "maker": "columbia", "family": "columbia/openfold3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4340, "dossier_id": "columbia/ssamba/ssamba-base", "name": "SSAMBA-Base", "maker": "columbia", "family": "columbia/ssamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4341, "dossier_id": "columbia/ssamba/ssamba-small", "name": "SSAMBA-Small", "maker": "columbia", "family": "columbia/ssamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4342, "dossier_id": "columbia/ssamba/ssamba-tiny", "name": "SSAMBA-Tiny", "maker": "columbia", "family": "columbia/ssamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4343, "dossier_id": "concordia/dycast/dycast", "name": "DyCAST", "maker": "concordia", "family": "concordia/dycast", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4344, "dossier_id": "concordia/resepformer/resepformer-wsj02mix", "name": "RE-SepFormer (WSJ0-2Mix)", "maker": "concordia", "family": "concordia/resepformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4345, "dossier_id": "concordia/zipcodec/zipcodec", "name": "ZipCodec", "maker": "concordia", "family": "concordia/zipcodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4346, "dossier_id": "contextual-ai/archangel/archangel-csft-llama13b", "name": "archangel_csft_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4347, "dossier_id": "contextual-ai/archangel/archangel-csft-llama30b", "name": "archangel_csft_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4348, "dossier_id": "contextual-ai/archangel/archangel-csft-llama7b", "name": "archangel_csft_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4349, "dossier_id": "contextual-ai/archangel/archangel-csft-pythia1-4b", "name": "archangel_csft_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4350, "dossier_id": "contextual-ai/archangel/archangel-csft-pythia12-0b", "name": "archangel_csft_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4351, "dossier_id": "contextual-ai/archangel/archangel-csft-pythia2-8b", "name": "archangel_csft_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4352, "dossier_id": "contextual-ai/archangel/archangel-csft-pythia6-9b", "name": "archangel_csft_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4353, "dossier_id": "contextual-ai/archangel/archangel-dpo-llama13b", "name": "archangel_dpo_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4354, "dossier_id": "contextual-ai/archangel/archangel-dpo-llama30b", "name": "archangel_dpo_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4355, "dossier_id": "contextual-ai/archangel/archangel-dpo-llama7b", "name": "archangel_dpo_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4356, "dossier_id": "contextual-ai/archangel/archangel-dpo-pythia1-4b", "name": "archangel_dpo_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4357, "dossier_id": "contextual-ai/archangel/archangel-dpo-pythia12-0b", "name": "archangel_dpo_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4358, "dossier_id": "contextual-ai/archangel/archangel-dpo-pythia2-8b", "name": "archangel_dpo_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4359, "dossier_id": "contextual-ai/archangel/archangel-dpo-pythia6-9b", "name": "archangel_dpo_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4360, "dossier_id": "contextual-ai/archangel/archangel-kto-llama13b", "name": "archangel_kto_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4361, "dossier_id": "contextual-ai/archangel/archangel-kto-llama30b", "name": "archangel_kto_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4362, "dossier_id": "contextual-ai/archangel/archangel-kto-llama7b", "name": "archangel_kto_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4363, "dossier_id": "contextual-ai/archangel/archangel-kto-pythia1-4b", "name": "archangel_kto_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4364, "dossier_id": "contextual-ai/archangel/archangel-kto-pythia12-0b", "name": "archangel_kto_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4365, "dossier_id": "contextual-ai/archangel/archangel-kto-pythia2-8b", "name": "archangel_kto_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4366, "dossier_id": "contextual-ai/archangel/archangel-kto-pythia6-9b", "name": "archangel_kto_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4367, "dossier_id": "contextual-ai/archangel/archangel-ppo-llama13b", "name": "archangel_ppo_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4368, "dossier_id": "contextual-ai/archangel/archangel-ppo-llama30b", "name": "archangel_ppo_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4369, "dossier_id": "contextual-ai/archangel/archangel-ppo-llama7b", "name": "archangel_ppo_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4370, "dossier_id": "contextual-ai/archangel/archangel-ppo-pythia1-4b", "name": "archangel_ppo_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4371, "dossier_id": "contextual-ai/archangel/archangel-ppo-pythia12-0b", "name": "archangel_ppo_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4372, "dossier_id": "contextual-ai/archangel/archangel-ppo-pythia2-8b", "name": "archangel_ppo_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4373, "dossier_id": "contextual-ai/archangel/archangel-ppo-pythia6-9b", "name": "archangel_ppo_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4374, "dossier_id": "contextual-ai/archangel/archangel-sft-csft-llama13b", "name": "archangel_sft-csft_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4375, "dossier_id": "contextual-ai/archangel/archangel-sft-csft-llama30b", "name": "archangel_sft-csft_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4376, "dossier_id": "contextual-ai/archangel/archangel-sft-csft-llama7b", "name": "archangel_sft-csft_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4377, "dossier_id": "contextual-ai/archangel/archangel-sft-csft-pythia1-4b", "name": "archangel_sft-csft_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4378, "dossier_id": "contextual-ai/archangel/archangel-sft-csft-pythia12-0b", "name": "archangel_sft-csft_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4379, "dossier_id": "contextual-ai/archangel/archangel-sft-csft-pythia2-8b", "name": "archangel_sft-csft_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4380, "dossier_id": "contextual-ai/archangel/archangel-sft-csft-pythia6-9b", "name": "archangel_sft-csft_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4381, "dossier_id": "contextual-ai/archangel/archangel-sft-dpo-llama13b", "name": "archangel_sft-dpo_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4382, "dossier_id": "contextual-ai/archangel/archangel-sft-dpo-llama30b", "name": "archangel_sft-dpo_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4383, "dossier_id": "contextual-ai/archangel/archangel-sft-dpo-llama7b", "name": "archangel_sft-dpo_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4384, "dossier_id": "contextual-ai/archangel/archangel-sft-dpo-pythia1-4b", "name": "archangel_sft-dpo_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4385, "dossier_id": "contextual-ai/archangel/archangel-sft-dpo-pythia12-0b", "name": "archangel_sft-dpo_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4386, "dossier_id": "contextual-ai/archangel/archangel-sft-dpo-pythia2-8b", "name": "archangel_sft-dpo_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4387, "dossier_id": "contextual-ai/archangel/archangel-sft-dpo-pythia6-9b", "name": "archangel_sft-dpo_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4388, "dossier_id": "contextual-ai/archangel/archangel-sft-kto-llama13b", "name": "archangel_sft-kto_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4389, "dossier_id": "contextual-ai/archangel/archangel-sft-kto-llama30b", "name": "archangel_sft-kto_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4390, "dossier_id": "contextual-ai/archangel/archangel-sft-kto-llama7b", "name": "archangel_sft-kto_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4391, "dossier_id": "contextual-ai/archangel/archangel-sft-kto-pythia1-4b", "name": "archangel_sft-kto_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4392, "dossier_id": "contextual-ai/archangel/archangel-sft-kto-pythia12-0b", "name": "archangel_sft-kto_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4393, "dossier_id": "contextual-ai/archangel/archangel-sft-kto-pythia2-8b", "name": "archangel_sft-kto_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4394, "dossier_id": "contextual-ai/archangel/archangel-sft-kto-pythia6-9b", "name": "archangel_sft-kto_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4395, "dossier_id": "contextual-ai/archangel/archangel-sft-llama13b", "name": "archangel_sft_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4396, "dossier_id": "contextual-ai/archangel/archangel-sft-llama30b", "name": "archangel_sft_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4397, "dossier_id": "contextual-ai/archangel/archangel-sft-llama7b", "name": "archangel_sft_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4398, "dossier_id": "contextual-ai/archangel/archangel-sft-ppo-llama13b", "name": "archangel_sft-ppo_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4399, "dossier_id": "contextual-ai/archangel/archangel-sft-ppo-llama30b", "name": "archangel_sft-ppo_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4400, "dossier_id": "contextual-ai/archangel/archangel-sft-ppo-llama7b", "name": "archangel_sft-ppo_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4401, "dossier_id": "contextual-ai/archangel/archangel-sft-ppo-pythia1-4b", "name": "archangel_sft-ppo_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4402, "dossier_id": "contextual-ai/archangel/archangel-sft-ppo-pythia12-0b", "name": "archangel_sft-ppo_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4403, "dossier_id": "contextual-ai/archangel/archangel-sft-ppo-pythia2-8b", "name": "archangel_sft-ppo_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4404, "dossier_id": "contextual-ai/archangel/archangel-sft-ppo-pythia6-9b", "name": "archangel_sft-ppo_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4405, "dossier_id": "contextual-ai/archangel/archangel-sft-pythia1-4b", "name": "archangel_sft_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4406, "dossier_id": "contextual-ai/archangel/archangel-sft-pythia12-0b", "name": "archangel_sft_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4407, "dossier_id": "contextual-ai/archangel/archangel-sft-pythia2-8b", "name": "archangel_sft_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4408, "dossier_id": "contextual-ai/archangel/archangel-sft-pythia6-9b", "name": "archangel_sft_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4409, "dossier_id": "contextual-ai/archangel/archangel-sft-slic-llama13b", "name": "archangel_sft-slic_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4410, "dossier_id": "contextual-ai/archangel/archangel-sft-slic-llama7b", "name": "archangel_sft-slic_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4411, "dossier_id": "contextual-ai/archangel/archangel-sft-slic-pythia1-4b", "name": "archangel_sft-slic_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4412, "dossier_id": "contextual-ai/archangel/archangel-sft-slic-pythia12-0b", "name": "archangel_sft-slic_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4413, "dossier_id": "contextual-ai/archangel/archangel-sft-slic-pythia2-8b", "name": "archangel_sft-slic_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4414, "dossier_id": "contextual-ai/archangel/archangel-sft-slic-pythia6-9b", "name": "archangel_sft-slic_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4415, "dossier_id": "contextual-ai/archangel/archangel-slic-llama13b", "name": "archangel_slic_llama13b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4416, "dossier_id": "contextual-ai/archangel/archangel-slic-llama30b", "name": "archangel_slic_llama30b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4417, "dossier_id": "contextual-ai/archangel/archangel-slic-llama7b", "name": "archangel_slic_llama7b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4418, "dossier_id": "contextual-ai/archangel/archangel-slic-pythia1-4b", "name": "archangel_slic_pythia1-4b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4419, "dossier_id": "contextual-ai/archangel/archangel-slic-pythia12-0b", "name": "archangel_slic_pythia12-0b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4420, "dossier_id": "contextual-ai/archangel/archangel-slic-pythia2-8b", "name": "archangel_slic_pythia2-8b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4421, "dossier_id": "contextual-ai/archangel/archangel-slic-pythia6-9b", "name": "archangel_slic_pythia6-9b", "maker": "contextual-ai", "family": "contextual-ai/archangel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4422, "dossier_id": "contextual-ai/contextual-sql/ctx-bird-reward-250121", "name": "Contextual-SQL reward model", "maker": "contextual-ai", "family": "contextual-ai/contextual-sql", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4423, "dossier_id": "contextual-ai/glm/glm-v1", "name": "GLM v1", "maker": "contextual-ai", "family": "contextual-ai/glm", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4424, "dossier_id": "contextual-ai/glm/glm-v2", "name": "GLM v2", "maker": "contextual-ai", "family": "contextual-ai/glm", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 15.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4425, "dossier_id": "contextual-ai/halo/contextual-kto-mistral-pairrm", "name": "Contextual KTO Mistral PairRM", "maker": "contextual-ai", "family": "contextual-ai/halo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4426, "dossier_id": "contextual-ai/lmunit/lmunit", "name": "LMUnit", "maker": "contextual-ai", "family": "contextual-ai/lmunit", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4427, "dossier_id": "contextual-ai/lmunit/lmunit-llama3-1-70b", "name": "LMUnit Llama 3.1 70B", "maker": "contextual-ai", "family": "contextual-ai/lmunit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4428, "dossier_id": "contextual-ai/lmunit/lmunit-qwen2-5-72b", "name": "LMUnit Qwen2.5 72B", "maker": "contextual-ai", "family": "contextual-ai/lmunit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4429, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v1-instruct", "name": "ctxl-rerank-v1-instruct", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4430, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual", "name": "ctxl-rerank-v2-instruct-multilingual", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4431, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b", "name": "Contextual Reranker v2 1B", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4432, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b-fp8", "name": "Contextual Reranker v2 1B FP8", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4433, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-1b-nvfp4", "name": "Contextual Reranker v2 1B NVFP4", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4434, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b", "name": "Contextual Reranker v2 2B", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4435, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b-fp8", "name": "Contextual Reranker v2 2B FP8", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4436, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-2b-nvfp4", "name": "Contextual Reranker v2 2B NVFP4", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4437, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b", "name": "Contextual Reranker v2 6B", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4438, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b-fp8", "name": "Contextual Reranker v2 6B FP8", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4439, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-6b-nvfp4", "name": "Contextual Reranker v2 6B NVFP4", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4440, "dossier_id": "contextual-ai/rerank/ctxl-rerank-v2-instruct-multilingual-mini", "name": "ctxl-rerank-v2-instruct-multilingual-mini", "maker": "contextual-ai", "family": "contextual-ai/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4441, "dossier_id": "cornell/birdnet/birdnet", "name": "BirdNET", "maker": "cornell", "family": "cornell/birdnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4442, "dossier_id": "cornell/birdnet/birdnet-plus", "name": "BirdNET+", "maker": "cornell", "family": "cornell/birdnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4443, "dossier_id": "cornell/cde/cde-small-v1", "name": "CDE-small-v1", "maker": "cornell", "family": "cornell/cde", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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: `.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4444, "dossier_id": "cornell/cde/cde-small-v2", "name": "CDE-small-v2", "maker": "cornell", "family": "cornell/cde", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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: `.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4445, "dossier_id": "cornell/densenet/densenet-121", "name": "DenseNet-121", "maker": "cornell", "family": "cornell/densenet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4446, "dossier_id": "cornell/densenet/densenet-161", "name": "DenseNet-161", "maker": "cornell", "family": "cornell/densenet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4447, "dossier_id": "cornell/densenet/densenet-169", "name": "DenseNet-169", "maker": "cornell", "family": "cornell/densenet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4448, "dossier_id": "cornell/densenet/densenet-201", "name": "DenseNet-201", "maker": "cornell", "family": "cornell/densenet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification. It is the deepest of the four original-paper ImageNet models that use growth rate 32.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4449, "dossier_id": "cornell/merlin/merlin-photo-id", "name": "Merlin Photo ID", "maker": "cornell", "family": "cornell/merlin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4450, "dossier_id": "cornell/merlin/merlin-sound-id", "name": "Merlin Sound ID", "maker": "cornell", "family": "cornell/merlin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4451, "dossier_id": "cornell/nighthawk/nighthawk", "name": "Nighthawk", "maker": "cornell", "family": "cornell/nighthawk", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4452, "dossier_id": "crete/mocapnet/mocapnet", "name": "MocapNET", "maker": "crete", "family": "crete/mocapnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4453, "dossier_id": "crete/y-map-net/y-map-net", "name": "Y-MAP-Net", "maker": "crete", "family": "crete/y-map-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4454, "dossier_id": "cuhk/acegpt/acegpt-13b", "name": "AceGPT-13B", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4455, "dossier_id": "cuhk/acegpt/acegpt-13b-chat", "name": "AceGPT-13B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4456, "dossier_id": "cuhk/acegpt/acegpt-7b", "name": "AceGPT-7B", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4457, "dossier_id": "cuhk/acegpt/acegpt-7b-chat", "name": "AceGPT-7B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4458, "dossier_id": "cuhk/acegpt/acegpt-v1-5-13b", "name": "AceGPT-v1.5-13B", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4459, "dossier_id": "cuhk/acegpt/acegpt-v1-5-13b-chat", "name": "AceGPT-v1.5-13B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4460, "dossier_id": "cuhk/acegpt/acegpt-v1-5-7b", "name": "AceGPT-v1.5-7B", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4461, "dossier_id": "cuhk/acegpt/acegpt-v1-5-7b-chat", "name": "AceGPT-v1.5-7B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4462, "dossier_id": "cuhk/acegpt/acegpt-v2-32b", "name": "AceGPT-v2-32B", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4463, "dossier_id": "cuhk/acegpt/acegpt-v2-32b-chat", "name": "AceGPT-v2-32B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4464, "dossier_id": "cuhk/acegpt/acegpt-v2-70b", "name": "AceGPT-v2-70B", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4465, "dossier_id": "cuhk/acegpt/acegpt-v2-70b-chat", "name": "AceGPT-v2-70B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4466, "dossier_id": "cuhk/acegpt/acegpt-v2-8b", "name": "AceGPT-v2-8B", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4467, "dossier_id": "cuhk/acegpt/acegpt-v2-8b-chat", "name": "AceGPT-v2-8B-Chat", "maker": "cuhk", "family": "cuhk/acegpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4468, "dossier_id": "cuhk/allava/allava-3b", "name": "ALLaVA-3B", "maker": "cuhk", "family": "cuhk/allava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4469, "dossier_id": "cuhk/allava/allava-phi2-2-7b", "name": "ALLaVA-Phi2-2.7B", "maker": "cuhk", "family": "cuhk/allava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4470, "dossier_id": "cuhk/allava/allava-phi3-mini-128k", "name": "ALLaVA-Phi3-mini-128k", "maker": "cuhk", "family": "cuhk/allava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4471, "dossier_id": "cuhk/allava/allava-stablelm2-1-6b", "name": "ALLaVA-StableLM2-1.6B", "maker": "cuhk", "family": "cuhk/allava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4472, "dossier_id": "cuhk/apollo/apollo-0-5b", "name": "Apollo-0.5B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4473, "dossier_id": "cuhk/apollo/apollo-1-8b", "name": "Apollo-1.8B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4474, "dossier_id": "cuhk/apollo/apollo-2b", "name": "Apollo-2B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4475, "dossier_id": "cuhk/apollo/apollo-34b", "name": "Apollo-34B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4476, "dossier_id": "cuhk/apollo/apollo-6b", "name": "Apollo-6B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4477, "dossier_id": "cuhk/apollo/apollo-72b", "name": "Apollo-72B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4478, "dossier_id": "cuhk/apollo/apollo-7b", "name": "Apollo-7B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4479, "dossier_id": "cuhk/apollo/apollo-medjamba", "name": "Apollo-MedJamba", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4480, "dossier_id": "cuhk/apollo/apollo-moe-0-5b", "name": "Apollo-MoE-0.5B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same 50-language medical question-answering job as Apollo2, with an upcycled MoE backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4481, "dossier_id": "cuhk/apollo/apollo-moe-1-5b", "name": "Apollo-MoE-1.5B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same 50-language medical question-answering job as Apollo2, with an upcycled MoE backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4482, "dossier_id": "cuhk/apollo/apollo-moe-7b", "name": "Apollo-MoE-7B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same 50-language medical question-answering job as Apollo2, with an upcycled MoE backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4483, "dossier_id": "cuhk/apollo/apollo2-0-5b", "name": "Apollo2-0.5B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4484, "dossier_id": "cuhk/apollo/apollo2-1-5b", "name": "Apollo2-1.5B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4485, "dossier_id": "cuhk/apollo/apollo2-2b", "name": "Apollo2-2B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4486, "dossier_id": "cuhk/apollo/apollo2-3-8b", "name": "Apollo2-3.8B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4487, "dossier_id": "cuhk/apollo/apollo2-7b", "name": "Apollo2-7B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4488, "dossier_id": "cuhk/apollo/apollo2-9b", "name": "Apollo2-9B", "maker": "cuhk", "family": "cuhk/apollo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical question answering across many languages. The paper was revised on 10 February 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4489, "dossier_id": "cuhk/blenderllm/blenderllm", "name": "BlenderLLM", "maker": "cuhk", "family": "cuhk/blenderllm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4490, "dossier_id": "cuhk/chimera/chimera-chat-13b-delta", "name": "Chimera-Chat-13B delta", "maker": "cuhk", "family": "cuhk/chimera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4491, "dossier_id": "cuhk/chimera/chimera-chat-7b-delta", "name": "Chimera-Chat-7B delta", "maker": "cuhk", "family": "cuhk/chimera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4492, "dossier_id": "cuhk/chimera/chimera-inst-chat-13b-delta", "name": "Chimera-Inst-Chat-13B delta", "maker": "cuhk", "family": "cuhk/chimera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4493, "dossier_id": "cuhk/chimera/chimera-inst-chat-7b-delta", "name": "Chimera-Inst-Chat-7B delta", "maker": "cuhk", "family": "cuhk/chimera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4494, "dossier_id": "cuhk/deear/deear-base", "name": "DeEAR Base", "maker": "cuhk", "family": "cuhk/deear", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4495, "dossier_id": "cuhk/diagnosisgpt/diagnosisgpt-34b", "name": "DiagnosisGPT-34B", "maker": "cuhk", "family": "cuhk/diagnosisgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Produces a diagnostic chain rather than only a final label. The abstract's disease coverage figure is 9604.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4496, "dossier_id": "cuhk/diagnosisgpt/diagnosisgpt-6b", "name": "DiagnosisGPT-6B", "maker": "cuhk", "family": "cuhk/diagnosisgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Produces a diagnostic chain rather than only a final label. The abstract's disease coverage figure is 9604.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4497, "dossier_id": "cuhk/echox/echox-3b", "name": "EchoX-3B", "maker": "cuhk", "family": "cuhk/echox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speech understanding and speech generation in English. The project page is the EchoX GitHub repository.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4498, "dossier_id": "cuhk/echox/echox-8b", "name": "EchoX-8B", "maker": "cuhk", "family": "cuhk/echox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speech understanding and speech generation in English. The project page is the EchoX GitHub repository.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4499, "dossier_id": "cuhk/grammargpt/grammargpt", "name": "GrammarGPT", "maker": "cuhk", "family": "cuhk/grammargpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repository name says grammar. The card body and any paper were not opened beyond the tag list.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4500, "dossier_id": "cuhk/himed/himed-8b", "name": "HiMed-8B", "maker": "cuhk", "family": "cuhk/himed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4501, "dossier_id": "cuhk/himed/himed-verifier", "name": "HiMed-Verifier", "maker": "cuhk", "family": "cuhk/himed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repository name says verifier. The scoring prompt was not copied.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4502, "dossier_id": "cuhk/huatuogpt/huatuogpt-13b", "name": "HuatuoGPT-13B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4503, "dossier_id": "cuhk/huatuogpt/huatuogpt-3-27b", "name": "HuatuoGPT-3-27B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4504, "dossier_id": "cuhk/huatuogpt/huatuogpt-3-32b", "name": "HuatuoGPT-3-32B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4505, "dossier_id": "cuhk/huatuogpt/huatuogpt-3-7b-pangu", "name": "HuatuoGPT-3-7B-Pangu", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4506, "dossier_id": "cuhk/huatuogpt/huatuogpt-3-8b", "name": "HuatuoGPT-3-8B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4507, "dossier_id": "cuhk/huatuogpt/huatuogpt-3-9b", "name": "HuatuoGPT-3-9B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4508, "dossier_id": "cuhk/huatuogpt/huatuogpt-3-grader-8b", "name": "HuatuoGPT-3 grader 8B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4509, "dossier_id": "cuhk/huatuogpt/huatuogpt-7b", "name": "HuatuoGPT-7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4510, "dossier_id": "cuhk/huatuogpt/huatuogpt-o1-70b", "name": "HuatuoGPT-o1-70B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4511, "dossier_id": "cuhk/huatuogpt/huatuogpt-o1-72b", "name": "HuatuoGPT-o1-72B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4512, "dossier_id": "cuhk/huatuogpt/huatuogpt-o1-7b", "name": "HuatuoGPT-o1-7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4513, "dossier_id": "cuhk/huatuogpt/huatuogpt-o1-8b", "name": "HuatuoGPT-o1-8B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4514, "dossier_id": "cuhk/huatuogpt/huatuogpt-reward-7b", "name": "HuatuoGPT reward model 7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scores medical responses for the HuatuoGPT RLAIF pipeline. It is a companion model, not the chat doctor.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4515, "dossier_id": "cuhk/huatuogpt/huatuogpt-vision-34b", "name": "HuatuoGPT-Vision-34B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical visual question answering. The lab's CLI takes a text query and one or more image paths.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4516, "dossier_id": "cuhk/huatuogpt/huatuogpt-vision-7b", "name": "HuatuoGPT-Vision-7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical visual question answering. The lab's CLI takes a text query and one or more image paths.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4517, "dossier_id": "cuhk/huatuogpt/huatuogpt-vision-7b-qwen2-5-vl", "name": "HuatuoGPT-Vision-7B-Qwen2.5VL", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same medical image question-answering job as HuatuoGPT-Vision, on a newer Qwen vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4518, "dossier_id": "cuhk/huatuogpt/huatuogpt2-13b", "name": "HuatuoGPT-II-13B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4519, "dossier_id": "cuhk/huatuogpt/huatuogpt2-34b", "name": "HuatuoGPT-II-34B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4520, "dossier_id": "cuhk/huatuogpt/huatuogpt2-7b", "name": "HuatuoGPT-II-7B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4521, "dossier_id": "cuhk/huatuogpt/medical-o1-verifier-3b", "name": "HuatuoGPT-o1 medical verifier 3B", "maker": "cuhk", "family": "cuhk/huatuogpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Checks verifiable medical answers during PPO. It is not the chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4522, "dossier_id": "cuhk/janus-4o/janus-4o-7b", "name": "Janus-4o-7B", "maker": "cuhk", "family": "cuhk/janus-4o", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4523, "dossier_id": "cuhk/lisa/lisa-13b", "name": "LISA-13B", "maker": "cuhk", "family": "cuhk/lisa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Segment the object described by a reasoning query, including queries that do not name the object directly.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4524, "dossier_id": "cuhk/lisa/lisa-7b", "name": "LISA-7B", "maker": "cuhk", "family": "cuhk/lisa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Segment the object described by a reasoning query, including queries that do not name the object directly.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4525, "dossier_id": "cuhk/liteflownet/liteflownet", "name": "LiteFlowNet", "maker": "cuhk", "family": "cuhk/liteflownet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Estimate optical flow between two frames. A third-party PyTorch reimplementation is not this release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4526, "dossier_id": "cuhk/longllava/longllava-53b-a13b", "name": "LongLLaVA-53B-A13B", "maker": "cuhk", "family": "cuhk/longllava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Visual question answering over many images. The paper's efficiency claim is nearly one thousand images on one 80GB A100.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4527, "dossier_id": "cuhk/longllava/longllava-9b", "name": "LongLLaVA-9B", "maker": "cuhk", "family": "cuhk/longllava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Visual question answering over many images. The paper's efficiency claim is nearly one thousand images on one 80GB A100.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4528, "dossier_id": "cuhk/longllava/longllavamed-9b", "name": "LongLLaVAMed-9B", "maker": "cuhk", "family": "cuhk/longllava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Visual question answering over many images. The paper's efficiency claim is nearly one thousand images on one 80GB A100.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4529, "dossier_id": "cuhk/medgen/medgen-1-3b", "name": "MedGen-1.3B", "maker": "cuhk", "family": "cuhk/medgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate medical video from text. Alibaba's own Wan checkpoints stay in the Alibaba catalog.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4530, "dossier_id": "cuhk/medgen/medgen-14b", "name": "MedGen-14B", "maker": "cuhk", "family": "cuhk/medgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate medical video from text. Alibaba's own Wan checkpoints stay in the Alibaba catalog.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4531, "dossier_id": "cuhk/mgm-omni/mgm-omni-32b", "name": "MGM-Omni-32B", "maker": "cuhk", "family": "cuhk/mgm-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4532, "dossier_id": "cuhk/mgm-omni/mgm-omni-7b", "name": "MGM-Omni-7B", "maker": "cuhk", "family": "cuhk/mgm-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4533, "dossier_id": "cuhk/mgm-omni/mgm-omni-tts-0-6b", "name": "MGM-Omni-TTS-0.6B", "maker": "cuhk", "family": "cuhk/mgm-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4534, "dossier_id": "cuhk/mgm-omni/mgm-omni-tts-2b", "name": "MGM-Omni-TTS-2B", "maker": "cuhk", "family": "cuhk/mgm-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4535, "dossier_id": "cuhk/mgm-omni/mgm-omni-tts-2b-0927", "name": "MGM-Omni-TTS-2B-0927", "maker": "cuhk", "family": "cuhk/mgm-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4536, "dossier_id": "cuhk/mgm-omni/mgm-omni-tts-4b", "name": "MGM-Omni-TTS-4B", "maker": "cuhk", "family": "cuhk/mgm-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4537, "dossier_id": "cuhk/mini-gemini/mgm-13b", "name": "Mini-Gemini-13B", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4538, "dossier_id": "cuhk/mini-gemini/mgm-13b-hd", "name": "Mini-Gemini-13B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4539, "dossier_id": "cuhk/mini-gemini/mgm-2b", "name": "Mini-Gemini-2B", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4540, "dossier_id": "cuhk/mini-gemini/mgm-34b", "name": "Mini-Gemini-34B", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4541, "dossier_id": "cuhk/mini-gemini/mgm-34b-hd", "name": "Mini-Gemini-34B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4542, "dossier_id": "cuhk/mini-gemini/mgm-7b", "name": "Mini-Gemini-7B", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4543, "dossier_id": "cuhk/mini-gemini/mgm-7b-hd", "name": "Mini-Gemini-7B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4544, "dossier_id": "cuhk/mini-gemini/mgm-8b", "name": "Mini-Gemini-8B", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4545, "dossier_id": "cuhk/mini-gemini/mgm-8b-hd", "name": "Mini-Gemini-8B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4546, "dossier_id": "cuhk/mini-gemini/mgm-8x7b", "name": "Mini-Gemini-8x7B", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4547, "dossier_id": "cuhk/mini-gemini/mgm-8x7b-hd", "name": "Mini-Gemini-8x7B-HD", "maker": "cuhk", "family": "cuhk/mini-gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4548, "dossier_id": "cuhk/phoenix/phoenix-chat-7b", "name": "Phoenix-Chat-7B", "maker": "cuhk", "family": "cuhk/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4549, "dossier_id": "cuhk/phoenix/phoenix-inst-chat-7b", "name": "Phoenix-Inst-Chat-7B", "maker": "cuhk", "family": "cuhk/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4550, "dossier_id": "cuhk/phoenix/phoenix-multiple-langs-v1", "name": "Phoenix multiple languages v1", "maker": "cuhk", "family": "cuhk/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4551, "dossier_id": "cuhk/platolm/platolm-7b", "name": "PlatoLM-7B", "maker": "cuhk", "family": "cuhk/platolm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4552, "dossier_id": "cuhk/psanet/psanet", "name": "PSANet", "maker": "cuhk", "family": "cuhk/psanet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Semantic scene parsing with point-wise spatial attention. Dataset checkpoints are versions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4553, "dossier_id": "cuhk/pspnet/pspnet", "name": "PSPNet", "maker": "cuhk", "family": "cuhk/pspnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Semantic scene parsing. Dataset-specific checkpoints are versions of one model, not separate products.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4554, "dossier_id": "cuhk/rag-instruct/rag-instruct-llama3-3b", "name": "RAG-Instruct-Llama3-3B", "maker": "cuhk", "family": "cuhk/rag-instruct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4555, "dossier_id": "cuhk/rag-instruct/rag-instruct-llama3-8b", "name": "RAG-Instruct-Llama3-8B", "maker": "cuhk", "family": "cuhk/rag-instruct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4556, "dossier_id": "cuhk/sepsisagent/sepsisagent-4b", "name": "SepsisAgent-4B", "maker": "cuhk", "family": "cuhk/sepsisagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4557, "dossier_id": "cuhk/shizhengpt/shizhengpt-32b-llm", "name": "ShizhenGPT-32B-LLM", "maker": "cuhk", "family": "cuhk/shizhengpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4558, "dossier_id": "cuhk/shizhengpt/shizhengpt-32b-vl", "name": "ShizhenGPT-32B-VL", "maker": "cuhk", "family": "cuhk/shizhengpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4559, "dossier_id": "cuhk/shizhengpt/shizhengpt-7b-llm", "name": "ShizhenGPT-7B-LLM", "maker": "cuhk", "family": "cuhk/shizhengpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4560, "dossier_id": "cuhk/shizhengpt/shizhengpt-7b-omni", "name": "ShizhenGPT-7B-Omni", "maker": "cuhk", "family": "cuhk/shizhengpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4561, "dossier_id": "cuhk/shizhengpt/shizhengpt-7b-vl", "name": "ShizhenGPT-7B-VL", "maker": "cuhk", "family": "cuhk/shizhengpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4562, "dossier_id": "cuhk/soundwave/soundwave", "name": "Soundwave", "maker": "cuhk", "family": "cuhk/soundwave", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4563, "dossier_id": "cuhk/srcnn/srcnn", "name": "SRCNN", "maker": "cuhk", "family": "cuhk/srcnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-image super-resolution. The Matlab test package and the Caffe training code are posted on the CUHK project page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4564, "dossier_id": "cuhk/tinydeepseek/tinydeepseek-0-5b-base", "name": "TinyDeepSeek-0.5B-base", "maker": "cuhk", "family": "cuhk/tinydeepseek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A compact DeepSeek-V3-style base checkpoint trained by the lab. Checkpoint-dump repositories are versions, not separate products.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4565, "dossier_id": "cuhk/tinydeepseek/tinydeepseek-3-3b-base", "name": "TinyDeepSeek-3.3B-base", "maker": "cuhk", "family": "cuhk/tinydeepseek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A compact DeepSeek-V3-style base checkpoint trained by the lab. Checkpoint-dump repositories are versions, not separate products.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4566, "dossier_id": "cuni/charles-translator/charles-translator", "name": "Charles Translator", "maker": "cuni", "family": "cuni/charles-translator", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4567, "dossier_id": "cuni/charles-translator/edukate-2025", "name": "EdUKate translation models 2025", "maker": "cuni", "family": "cuni/charles-translator", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4568, "dossier_id": "cuni/corpipe/corpipe23", "name": "CorPipe 23", "maker": "cuni", "family": "cuni/corpipe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It is the 2023 CorPipe release: one multilingual checkpoint rather than the later base, large, and xl split.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4569, "dossier_id": "cuni/corpipe/corpipe25", "name": "CorPipe 25", "maker": "cuni", "family": "cuni/corpipe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4570, "dossier_id": "cuni/corpipe/corpipe26", "name": "CorPipe 26", "maker": "cuni", "family": "cuni/corpipe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4571, "dossier_id": "cuni/corpipe/crac2026-empty-nodes", "name": "CRAC 2026 empty-nodes baseline", "maker": "cuni", "family": "cuni/corpipe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4572, "dossier_id": "cuni/cubbitt/cubbitt", "name": "CUBBITT", "maker": "cuni", "family": "cuni/cubbitt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4573, "dossier_id": "cuni/dama/dama-13b", "name": "DAMA 13B", "maker": "cuni", "family": "cuni/dama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4574, "dossier_id": "cuni/dama/dama-33b", "name": "DAMA 33B", "maker": "cuni", "family": "cuni/dama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4575, "dossier_id": "cuni/dama/dama-65b", "name": "DAMA 65B", "maker": "cuni", "family": "cuni/dama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4576, "dossier_id": "cuni/dama/dama-7b", "name": "DAMA 7B", "maker": "cuni", "family": "cuni/dama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4577, "dossier_id": "cuni/historical-page/clip-historical-page", "name": "Historical page classifier (CLIP)", "maker": "cuni", "family": "cuni/historical-page", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4578, "dossier_id": "cuni/historical-page/vit-historical-page", "name": "Historical page classifier (ViT line)", "maker": "cuni", "family": "cuni/historical-page", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4579, "dossier_id": "cuni/malach/labse-malach-multilabel", "name": "LaBSE-MALACH-Multilabel", "maker": "cuni", "family": "cuni/malach", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4580, "dossier_id": "cuni/malach/xlm-roberta-ehri-malach-ner", "name": "XLM-RoBERTa EHRI-MALACH NER", "maker": "cuni", "family": "cuni/malach", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4581, "dossier_id": "cuni/malach/xlm-roberta-malach", "name": "XLM-RoBERTa-MALACH", "maker": "cuni", "family": "cuni/malach", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4582, "dossier_id": "cuni/mate/akces-mate", "name": "MATE AKCES", "maker": "cuni", "family": "cuni/mate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4583, "dossier_id": "cuni/mate/geccc-mate", "name": "MATE GECCC", "maker": "cuni", "family": "cuni/mate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4584, "dossier_id": "cuni/multilexnorm/byt5-small-multilexnorm", "name": "UFAL ByT5-small MultiLexNorm", "maker": "cuni", "family": "cuni/multilexnorm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4585, "dossier_id": "cuni/nametag/nametag-3", "name": "NameTag 3", "maker": "cuni", "family": "cuni/nametag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4586, "dossier_id": "cuni/udify/udify", "name": "UDify", "maker": "cuni", "family": "cuni/udify", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4587, "dossier_id": "cuni/udpipe/udpipe-2", "name": "UDPipe 2", "maker": "cuni", "family": "cuni/udpipe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4588, "dossier_id": "cuni/wmt/wmt25-cuni-sft", "name": "CUNI-SFT", "maker": "cuni", "family": "cuni/wmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4589, "dossier_id": "cyberagent/calm/calm2-7b-chat-dpo-experimental", "name": "CALM2-7B-Chat DPO experimental", "maker": "cyberagent", "family": "cyberagent/calm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4590, "dossier_id": "cyberagent/calm/calm3-22b-chat-selfimprove-experimental", "name": "CALM3-22B-Chat self-improve experimental", "maker": "cyberagent", "family": "cyberagent/calm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4591, "dossier_id": "cyberagent/cat/cat-thinking-8b", "name": "CAT-Thinking-8B", "maker": "cyberagent", "family": "cyberagent/cat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4592, "dossier_id": "cyberagent/cat/cat-translate-0-8b", "name": "CAT-Translate-0.8B", "maker": "cyberagent", "family": "cyberagent/cat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4593, "dossier_id": "cyberagent/cat/cat-translate-1-4b", "name": "CAT-Translate-1.4B", "maker": "cyberagent", "family": "cyberagent/cat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4594, "dossier_id": "cyberagent/cat/cat-translate-3-3b", "name": "CAT-Translate-3.3B", "maker": "cyberagent", "family": "cyberagent/cat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4595, "dossier_id": "cyberagent/cat/cat-translate-3-3b-beta", "name": "CAT-Translate-3.3B-beta", "maker": "cyberagent", "family": "cyberagent/cat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4596, "dossier_id": "cyberagent/cat/cat-translate-7b", "name": "CAT-Translate-7B", "maker": "cyberagent", "family": "cyberagent/cat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4597, "dossier_id": "cyberagent/deepseek/deepseek-r1-distill-qwen-14b-japanese", "name": "DeepSeek-R1-Distill-Qwen-14B-Japanese", "maker": "cyberagent", "family": "cyberagent/deepseek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4598, "dossier_id": "cyberagent/deepseek/deepseek-r1-distill-qwen-32b-japanese", "name": "DeepSeek-R1-Distill-Qwen-32B-Japanese", "maker": "cyberagent", "family": "cyberagent/deepseek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4599, "dossier_id": "cyberagent/layerd/layerd-birefnet", "name": "LayerD BiRefNet", "maker": "cyberagent", "family": "cyberagent/layerd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4600, "dossier_id": "cyberagent/llama/llama-3-1-70b-japanese-instruct-2407", "name": "Llama-3.1-70B-Japanese-Instruct-2407", "maker": "cyberagent", "family": "cyberagent/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4601, "dossier_id": "cyberagent/llava/llava-calm2-siglip", "name": "LLaVA-CALM2-SigLIP", "maker": "cyberagent", "family": "cyberagent/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4602, "dossier_id": "cyberagent/markupdm/ldm-vq-f16-rgba", "name": "LDM VQ-F16 RGBA", "maker": "cyberagent", "family": "cyberagent/markupdm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it compresses RGBA images, including transparency, into discrete codes for graphic-design completion.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4603, "dossier_id": "cyberagent/markupdm/markupdm", "name": "MarkupDM", "maker": "cyberagent", "family": "cyberagent/markupdm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4604, "dossier_id": "cyberagent/mistral/mistral-nemo-japanese-instruct-2408", "name": "Mistral-Nemo-Japanese-Instruct-2408", "maker": "cyberagent", "family": "cyberagent/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card describes a Japanese continually pre-trained instruct model for chat, using a ChatML prompt.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4605, "dossier_id": "cyberagent/open-calm/open-calm-3b", "name": "OpenCALM-3B", "maker": "cyberagent", "family": "cyberagent/open-calm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4606, "dossier_id": "cyberagent/open-calm/open-calm-7b", "name": "OpenCALM-7B", "maker": "cyberagent", "family": "cyberagent/open-calm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4607, "dossier_id": "cyberagent/open-calm/open-calm-large", "name": "OpenCALM-Large", "maker": "cyberagent", "family": "cyberagent/open-calm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4608, "dossier_id": "cyberagent/open-calm/open-calm-medium", "name": "OpenCALM-Medium", "maker": "cyberagent", "family": "cyberagent/open-calm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4609, "dossier_id": "cyberagent/open-calm/open-calm-small", "name": "OpenCALM-Small", "maker": "cyberagent", "family": "cyberagent/open-calm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4610, "dossier_id": "d-id/live-portrait/live-portrait", "name": "Live Portrait", "maker": "d-id", "family": "d-id/live-portrait", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4611, "dossier_id": "d-id/speaking-portrait/trained-character", "name": "Trained Character", "maker": "d-id", "family": "d-id/speaking-portrait", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4612, "dossier_id": "d-id/translate/video-translate", "name": "Video Translate", "maker": "d-id", "family": "d-id/translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 1.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4613, "dossier_id": "d-id/v2/v2-photo-avatar", "name": "V2 Photo Avatar", "maker": "d-id", "family": "d-id/v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 1.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4614, "dossier_id": "d-id/v3/v3-instant-avatar", "name": "V3 Instant Avatar", "maker": "d-id", "family": "d-id/v3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 1.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4615, "dossier_id": "d-id/v3/v3-pro-avatar", "name": "V3 Pro Avatar", "maker": "d-id", "family": "d-id/v3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 1.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4616, "dossier_id": "d-id/v4/v4-expressive-avatar", "name": "V4 Expressive Avatar", "maker": "d-id", "family": "d-id/v4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 1.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4617, "dossier_id": "dalhousie/benthicnet/benthicnet-bt-rn50-e400", "name": "BenthicNet BT ResNet-50 400-epoch (Training)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4618, "dossier_id": "dalhousie/benthicnet/benthicnet-bt-rn50-e600-prod", "name": "BenthicNet BT ResNet-50 600-epoch (Production)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4619, "dossier_id": "dalhousie/benthicnet/benthicnet-mcv3-vit-b-e100", "name": "BenthicNet MoCo-v3 ViT-B 100-epoch (Training)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4620, "dossier_id": "dalhousie/benthicnet/benthicnet-mcv3-vit-b-e100-prod", "name": "BenthicNet MoCo-v3 ViT-B 100-epoch (Production)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4621, "dossier_id": "dalhousie/benthicnet/benthicnet-mcv3-vit-s-e200-prod", "name": "BenthicNet MoCo-v3 ViT-S 200-epoch (Production)", "maker": "dalhousie", "family": "dalhousie/benthicnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4622, "dossier_id": "decart/avatar/live-avatar", "name": "Live Avatar", "maker": "decart", "family": "decart/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4623, "dossier_id": "decart/lipsync/lipsync-live", "name": "LipSync Live", "maker": "decart", "family": "decart/lipsync", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4624, "dossier_id": "decart/lucy/lucy-14b", "name": "Lucy-14B", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4625, "dossier_id": "decart/lucy/lucy-2", "name": "Lucy 2", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4626, "dossier_id": "decart/lucy/lucy-2-1", "name": "Lucy 2.1", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4627, "dossier_id": "decart/lucy/lucy-2-1-vton", "name": "Lucy 2.1 VTON", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4628, "dossier_id": "decart/lucy/lucy-2-5", "name": "Lucy 2.5", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4629, "dossier_id": "decart/lucy/lucy-dev-i2v", "name": "Lucy Dev I2V", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4630, "dossier_id": "decart/lucy/lucy-edit-1-1-dev", "name": "Lucy Edit 1.1 Dev", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4631, "dossier_id": "decart/lucy/lucy-edit-dev", "name": "Lucy Edit Dev", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4632, "dossier_id": "decart/lucy/lucy-edit-fast", "name": "Lucy Edit Fast", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4633, "dossier_id": "decart/lucy/lucy-edit-live", "name": "Lucy Edit Live", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4634, "dossier_id": "decart/lucy/lucy-edit-pro", "name": "Lucy Edit Pro", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4635, "dossier_id": "decart/lucy/lucy-motion", "name": "Lucy Motion", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4636, "dossier_id": "decart/lucy/lucy-pro-flf2v", "name": "Lucy Pro FLF2V", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4637, "dossier_id": "decart/lucy/lucy-pro-i2v", "name": "Lucy Pro I2V", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4638, "dossier_id": "decart/lucy/lucy-pro-t2v", "name": "Lucy Pro T2V", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4639, "dossier_id": "decart/lucy/lucy-restyle-2", "name": "Lucy Restyle 2", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4640, "dossier_id": "decart/lucy/lucy-vton-2", "name": "Lucy VTON 2", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4641, "dossier_id": "decart/lucy/lucy-vton-3", "name": "Lucy VTON 3", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4642, "dossier_id": "decart/lucy/lucy-vton-3-5", "name": "Lucy VTON 3.5", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4643, "dossier_id": "decart/lucy/lucy-vton-3-6", "name": "Lucy VTON 3.6", "maker": "decart", "family": "decart/lucy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4644, "dossier_id": "decart/mirage/mirage-lsd", "name": "MirageLSD", "maker": "decart", "family": "decart/mirage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4645, "dossier_id": "decart/oasis/oasis", "name": "Oasis", "maker": "decart", "family": "decart/oasis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4646, "dossier_id": "decart/oasis/oasis-3-preview", "name": "Oasis 3 Preview", "maker": "decart", "family": "decart/oasis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4647, "dossier_id": "deepgram/aura/aura", "name": "Aura", "maker": "deepgram", "family": "deepgram/aura", "context_length": null, "prompt_price_per_m": 0.015, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4648, "dossier_id": "deepgram/aura/aura-2", "name": "Aura-2", "maker": "deepgram", "family": "deepgram/aura", "context_length": null, "prompt_price_per_m": 0.03, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4649, "dossier_id": "deepgram/base/base", "name": "Base", "maker": "deepgram", "family": "deepgram/base", "context_length": null, "prompt_price_per_m": 0.87, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4650, "dossier_id": "deepgram/base/base-conversationalai", "name": "Base Conversational AI", "maker": "deepgram", "family": "deepgram/base", "context_length": null, "prompt_price_per_m": 0.87, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A person talking to a bot, such as IVR or a kiosk.", "summary_intro": "Deepgram legacy Base domain model, API id base-conversationalai. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4651, "dossier_id": "deepgram/base/base-finance", "name": "Base Finance", "maker": "deepgram", "family": "deepgram/base", "context_length": null, "prompt_price_per_m": 0.87, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-speaker, uneven audio, finance vocabulary.", "summary_intro": "Deepgram legacy Base domain model, API id base-finance. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4652, "dossier_id": "deepgram/base/base-meeting", "name": "Base Meeting", "maker": "deepgram", "family": "deepgram/base", "context_length": null, "prompt_price_per_m": 0.87, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Conference-room audio, several speakers, one microphone.", "summary_intro": "Deepgram legacy Base domain model, API id base-meeting. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4653, "dossier_id": "deepgram/base/base-phonecall", "name": "Base Phonecall", "maker": "deepgram", "family": "deepgram/base", "context_length": null, "prompt_price_per_m": 0.87, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-bandwidth telephone audio.", "summary_intro": "Deepgram legacy Base domain model, API id base-phonecall. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4654, "dossier_id": "deepgram/base/base-video", "name": "Base Video", "maker": "deepgram", "family": "deepgram/base", "context_length": null, "prompt_price_per_m": 0.87, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Audio sourced from video.", "summary_intro": "Deepgram legacy Base domain model, API id base-video. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4655, "dossier_id": "deepgram/base/base-voicemail", "name": "Base Voicemail", "maker": "deepgram", "family": "deepgram/base", "context_length": null, "prompt_price_per_m": 0.87, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Short single-speaker low-bandwidth clips. Deepgram says this option is derived from the phonecall model.", "summary_intro": "Deepgram legacy Base domain model, API id base-voicemail. English (en, en-US). Still listed. Closed weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4656, "dossier_id": "deepgram/enhanced/enhanced", "name": "Enhanced", "maker": "deepgram", "family": "deepgram/enhanced", "context_length": null, "prompt_price_per_m": 0.99, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4657, "dossier_id": "deepgram/enhanced/enhanced-finance", "name": "Enhanced Finance", "maker": "deepgram", "family": "deepgram/enhanced", "context_length": null, "prompt_price_per_m": 0.99, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-speaker, uneven audio, finance vocabulary. The model-options page marks finance as beta.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4658, "dossier_id": "deepgram/enhanced/enhanced-meeting", "name": "Enhanced Meeting", "maker": "deepgram", "family": "deepgram/enhanced", "context_length": null, "prompt_price_per_m": 0.99, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Conference-room audio, several speakers, one microphone. The model-options page marks meeting as beta.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4659, "dossier_id": "deepgram/enhanced/enhanced-phonecall", "name": "Enhanced Phonecall", "maker": "deepgram", "family": "deepgram/enhanced", "context_length": null, "prompt_price_per_m": 0.99, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-bandwidth telephone audio.", "summary_intro": "Deepgram legacy Enhanced domain model, API id enhanced-phonecall. English. Not marked beta. Closed weights. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4660, "dossier_id": "deepgram/flux/flux-general-en", "name": "Flux General English", "maker": "deepgram", "family": "deepgram/flux", "context_length": null, "prompt_price_per_m": 0.0065, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4661, "dossier_id": "deepgram/flux/flux-general-multi", "name": "Flux General Multilingual", "maker": "deepgram", "family": "deepgram/flux", "context_length": null, "prompt_price_per_m": 0.0078, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4662, "dossier_id": "deepgram/flux-tts/flux-tts", "name": "Flux TTS", "maker": "deepgram", "family": "deepgram/flux-tts", "context_length": null, "prompt_price_per_m": 0.045, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4663, "dossier_id": "deepgram/nova/nova", "name": "Nova", "maker": "deepgram", "family": "deepgram/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4664, "dossier_id": "deepgram/nova/nova-medical", "name": "Nova Medical", "maker": "deepgram", "family": "deepgram/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4665, "dossier_id": "deepgram/nova/nova-phonecall", "name": "Nova Phonecall", "maker": "deepgram", "family": "deepgram/nova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deepgram says this option is for low-bandwidth phone audio on the original Nova model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4666, "dossier_id": "deepgram/nova-2/nova-2", "name": "Nova-2", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4667, "dossier_id": "deepgram/nova-2/nova-2-atc", "name": "Nova-2 Air Traffic Control", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for air traffic control audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4668, "dossier_id": "deepgram/nova-2/nova-2-automotive", "name": "Nova-2 Automotive", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for automotive vocabulary. The model-options page spells the domain automative.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4669, "dossier_id": "deepgram/nova-2/nova-2-conversationalai", "name": "Nova-2 Conversational AI", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for a person talking to a bot, such as IVR, a voice assistant, or a kiosk.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4670, "dossier_id": "deepgram/nova-2/nova-2-drivethru", "name": "Nova-2 Drive-thru", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for drive-thru audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4671, "dossier_id": "deepgram/nova-2/nova-2-finance", "name": "Nova-2 Finance", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for multi-speaker calls with uneven audio and finance vocabulary, such as earnings calls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4672, "dossier_id": "deepgram/nova-2/nova-2-medical", "name": "Nova-2 Medical", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for medical vocabulary. English only. Nova-3 Medical is the later medical model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4673, "dossier_id": "deepgram/nova-2/nova-2-meeting", "name": "Nova-2 Meeting", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for conference rooms: several speakers, one microphone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4674, "dossier_id": "deepgram/nova-2/nova-2-phonecall", "name": "Nova-2 Phonecall", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for low-bandwidth telephone audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4675, "dossier_id": "deepgram/nova-2/nova-2-video", "name": "Nova-2 Video", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for audio taken from video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4676, "dossier_id": "deepgram/nova-2/nova-2-voicemail", "name": "Nova-2 Voicemail", "maker": "deepgram", "family": "deepgram/nova-2", "context_length": null, "prompt_price_per_m": 0.35, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for short, single-speaker, low-bandwidth clips. Deepgram says it is derived from the phonecall model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4677, "dossier_id": "deepgram/nova-3/nova-3", "name": "Nova-3", "maker": "deepgram", "family": "deepgram/nova-3", "context_length": null, "prompt_price_per_m": 0.0048, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4678, "dossier_id": "deepgram/nova-3/nova-3-medical", "name": "Nova-3 Medical", "maker": "deepgram", "family": "deepgram/nova-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4679, "dossier_id": "deepgram/nova-3/nova-3-pharma", "name": "Nova-3 Pharma", "maker": "deepgram", "family": "deepgram/nova-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4680, "dossier_id": "deepl/translator/classic", "name": "DeepL Classic language model", "maker": "deepl", "family": "deepl/translator", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4681, "dossier_id": "deepl/translator/next-gen", "name": "DeepL next-generation language model", "maker": "deepl", "family": "deepl/translator", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4682, "dossier_id": "deepseek/coder/coder-7b-v1-5", "name": "DeepSeek-Coder-7B-v1.5", "maker": "deepseek", "family": "deepseek/coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Coder-7B-v1.5 is a January 2024 coder checkpoint. DeepSeekMath was initialized from its base model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4683, "dossier_id": "deepseek/janus/janus-1-3b", "name": "Janus-1.3B", "maker": "deepseek", "family": "deepseek/janus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Janus-1.3B is DeepSeek's October 2024 unified model for image understanding and image generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4684, "dossier_id": "deepseek/janus/janus-pro-1b", "name": "Janus-Pro-1B", "maker": "deepseek", "family": "deepseek/janus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Janus-Pro-1B is the smaller Janus-Pro model, for understanding and text-to-image generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4685, "dossier_id": "deepseek/janus/janus-pro-7b", "name": "Janus-Pro-7B", "maker": "deepseek", "family": "deepseek/janus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Janus-Pro-7B is the larger Janus-Pro unified understanding and generation model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4686, "dossier_id": "deepseek/janus/janusflow-1-3b", "name": "JanusFlow-1.3B", "maker": "deepseek", "family": "deepseek/janus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4687, "dossier_id": "deepseek/math/math-v2", "name": "DeepSeekMath-V2", "maker": "deepseek", "family": "deepseek/math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4688, "dossier_id": "deepseek/moe/moe-16b", "name": "DeepSeekMoE-16B", "maker": "deepseek", "family": "deepseek/moe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4689, "dossier_id": "deepseek/ocr/ocr", "name": "DeepSeek-OCR", "maker": "deepseek", "family": "deepseek/ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4690, "dossier_id": "deepseek/ocr/ocr-2", "name": "DeepSeek-OCR 2", "maker": "deepseek", "family": "deepseek/ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4691, "dossier_id": "deepseek/prover/prover-v1", "name": "DeepSeek-Prover-V1", "maker": "deepseek", "family": "deepseek/prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Prover-V1 is DeepSeek's first formal-math prover checkpoint on Hugging Face.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4692, "dossier_id": "deepseek/prover/prover-v1-5", "name": "DeepSeek-Prover-V1.5", "maker": "deepseek", "family": "deepseek/prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Prover-V1.5 is the August 2024 prover. DeepSeek released base, supervised, and reinforcement-learning checkpoints.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4693, "dossier_id": "deepseek/prover/prover-v2-671b", "name": "DeepSeek-Prover-V2-671B", "maker": "deepseek", "family": "deepseek/prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Prover-V2-671B is the large April 2025 prover, in the DeepSeek-V3 architecture family.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4694, "dossier_id": "deepseek/prover/prover-v2-7b", "name": "DeepSeek-Prover-V2-7B", "maker": "deepseek", "family": "deepseek/prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-Prover-V2-7B is the smaller April 2025 prover.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4695, "dossier_id": "deepseek/r1/r1-0528-qwen3-8b", "name": "DeepSeek-R1-0528-Qwen3-8B", "maker": "deepseek", "family": "deepseek/r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4696, "dossier_id": "deepseek/r1/r1-distill-llama-70b", "name": "DeepSeek-R1-Distill-Llama-70B", "maker": "deepseek", "family": "deepseek/r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4697, "dossier_id": "deepseek/r1/r1-distill-llama-8b", "name": "DeepSeek-R1-Distill-Llama-8B", "maker": "deepseek", "family": "deepseek/r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4698, "dossier_id": "deepseek/r1/r1-distill-qwen-1-5b", "name": "DeepSeek-R1-Distill-Qwen-1.5B", "maker": "deepseek", "family": "deepseek/r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4699, "dossier_id": "deepseek/r1/r1-distill-qwen-14b", "name": "DeepSeek-R1-Distill-Qwen-14B", "maker": "deepseek", "family": "deepseek/r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4700, "dossier_id": "deepseek/r1/r1-distill-qwen-32b", "name": "DeepSeek-R1-Distill-Qwen-32B", "maker": "deepseek", "family": "deepseek/r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4701, "dossier_id": "deepseek/r1/r1-distill-qwen-7b", "name": "DeepSeek-R1-Distill-Qwen-7B", "maker": "deepseek", "family": "deepseek/r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4702, "dossier_id": "deepseek/reasoner/deepseek-reasoner", "name": "deepseek-reasoner", "maker": "deepseek", "family": "deepseek/reasoner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4703, "dossier_id": "deepseek/v4/v4-1-pro", "name": "DeepSeek-V4.1-Pro", "maker": "deepseek", "family": "deepseek/v4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4704, "dossier_id": "deepseek/vl/vl-1-3b", "name": "DeepSeek-VL-1.3B", "maker": "deepseek", "family": "deepseek/vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-VL 1.3B is the small model in the first vision-language release. Base and chat weights are public.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4705, "dossier_id": "deepseek/vl/vl-7b", "name": "DeepSeek-VL-7B", "maker": "deepseek", "family": "deepseek/vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepSeek-VL 7B is the larger first vision-language model. Base and chat weights are public.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4706, "dossier_id": "delft/ap-mae/ap-mae-sc2-15b", "name": "AP-MAE-SC2-15B", "maker": "delft", "family": "delft/ap-mae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model AP-MAE-SC2-15B (ap-mae-sc2-15b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4707, "dossier_id": "delft/ap-mae/ap-mae-sc2-3b", "name": "AP-MAE-SC2-3B", "maker": "delft", "family": "delft/ap-mae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model AP-MAE-SC2-3B (ap-mae-sc2-3b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4708, "dossier_id": "delft/ap-mae/ap-mae-sc2-7b", "name": "AP-MAE-SC2-7B", "maker": "delft", "family": "delft/ap-mae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model AP-MAE-SC2-7B (ap-mae-sc2-7b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4709, "dossier_id": "delft/crossview/ccvpe", "name": "CCVPE", "maker": "delft", "family": "delft/crossview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model CCVPE (ccvpe). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4710, "dossier_id": "delft/crossview/crossview-metric-localization", "name": "Cross-View Metric Localization", "maker": "delft", "family": "delft/crossview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model Cross-View Metric Localization (crossview-metric-localization). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4711, "dossier_id": "delft/terraseg/terraseg-b", "name": "TerraSeg-B", "maker": "delft", "family": "delft/terraseg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model TerraSeg-B (terraseg-b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4712, "dossier_id": "delft/terraseg/terraseg-s", "name": "TerraSeg-S", "maker": "delft", "family": "delft/terraseg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model TerraSeg-S (terraseg-s). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4713, "dossier_id": "delft/unibev/unibev", "name": "UniBEV", "maker": "delft", "family": "delft/unibev", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model UniBEV (unibev). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4714, "dossier_id": "delft/voteflow/voteflow", "name": "VoteFlow", "maker": "delft", "family": "delft/voteflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Delft University of Technology model VoteFlow (voteflow). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4715, "dossier_id": "descript/cargan/cargan", "name": "CARGAN", "maker": "descript", "family": "descript/cargan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4716, "dossier_id": "descript/dac/dac", "name": "Descript Audio Codec", "maker": "descript", "family": "descript/dac", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4717, "dossier_id": "descript/eye-contact/eye-contact", "name": "Eye Contact", "maker": "descript", "family": "descript/eye-contact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4718, "dossier_id": "descript/green-screen/green-screen", "name": "Green Screen", "maker": "descript", "family": "descript/green-screen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4719, "dossier_id": "descript/jumpcut/jumpcut-smoothing", "name": "Jumpcut Smoothing", "maker": "descript", "family": "descript/jumpcut", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4720, "dossier_id": "descript/melgan/melgan", "name": "MelGAN", "maker": "descript", "family": "descript/melgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4721, "dossier_id": "descript/nu-gan/nu-gan", "name": "NU-GAN", "maker": "descript", "family": "descript/nu-gan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4722, "dossier_id": "descript/overdub/overdub", "name": "Overdub", "maker": "descript", "family": "descript/overdub", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4723, "dossier_id": "descript/regenerate/audio-regenerate", "name": "Audio Regenerate", "maker": "descript", "family": "descript/regenerate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4724, "dossier_id": "descript/regenerate/video-regenerate", "name": "Video Regenerate", "maker": "descript", "family": "descript/regenerate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4725, "dossier_id": "descript/studio-sound/studio-sound", "name": "Studio Sound", "maker": "descript", "family": "descript/studio-sound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4726, "dossier_id": "drexel/metadata/drexel-metadata-generator", "name": "Drexel Metadata Generator", "maker": "drexel", "family": "drexel/metadata", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4727, "dossier_id": "dtu/abepitope/abepitope", "name": "AbEpiTope 1.0", "maker": "dtu", "family": "dtu/abepitope", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4728, "dossier_id": "dtu/anoxpepred/anoxpepred", "name": "AnOxPePred 1.0", "maker": "dtu", "family": "dtu/anoxpepred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4729, "dossier_id": "dtu/arraypitope/arraypitope", "name": "ArrayPitope 1.0", "maker": "dtu", "family": "dtu/arraypitope", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4730, "dossier_id": "dtu/atom-jepa/atom-jepa", "name": "Atom-JEPA", "maker": "dtu", "family": "dtu/atom-jepa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4731, "dossier_id": "dtu/bepipred/bepipred", "name": "BepiPred 3.0", "maker": "dtu", "family": "dtu/bepipred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model BepiPred, current release 3.0, for B-cell epitope prediction from sequence. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4732, "dossier_id": "dtu/chlorop/chlorop", "name": "ChloroP 1.1", "maker": "dtu", "family": "dtu/chlorop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4733, "dossier_id": "dtu/cofactory/cofactory", "name": "Cofactory 1.0", "maker": "dtu", "family": "dtu/cofactory", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4734, "dossier_id": "dtu/deepcds/deepcds", "name": "DeepCDS 1.0", "maker": "dtu", "family": "dtu/deepcds", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4735, "dossier_id": "dtu/deeploc/deeploc", "name": "DeepLoc 2.1", "maker": "dtu", "family": "dtu/deeploc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model DeepLoc, current release 2.1, for eukaryotic protein localization. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4736, "dossier_id": "dtu/deeploc/deeplocpro", "name": "DeepLocPro 1.0", "maker": "dtu", "family": "dtu/deeploc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model DeepLocPro 1.0, the prokaryotic localization server paired with DeepLoc. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4737, "dossier_id": "dtu/deeptmhmm/deeptmhmm", "name": "DeepTMHMM 1.0", "maker": "dtu", "family": "dtu/deeptmhmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model DeepTMHMM 1.0, the deep-learning successor to TMHMM. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4738, "dossier_id": "dtu/dictyoglyc/dictyoglyc", "name": "DictyOGlyc 1.1", "maker": "dtu", "family": "dtu/dictyoglyc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4739, "dossier_id": "dtu/discotope/discotope", "name": "DiscoTope 3.0", "maker": "dtu", "family": "dtu/discotope", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model DiscoTope, current release 3.0, for B-cell epitopes from structure. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4740, "dossier_id": "dtu/distancep/distancep", "name": "DistanceP 1.0", "maker": "dtu", "family": "dtu/distancep", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predicts protein distance constraints. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4741, "dossier_id": "dtu/easygene/easygene", "name": "EasyGene 1.2", "maker": "dtu", "family": "dtu/easygene", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Gene finding in prokaryotes. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4742, "dossier_id": "dtu/haplocart/haplocart", "name": "HaploCart 1.0", "maker": "dtu", "family": "dtu/haplocart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4743, "dossier_id": "dtu/hlaassoc/hlaassoc", "name": "HLAAssoc 1.0", "maker": "dtu", "family": "dtu/hlaassoc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4744, "dossier_id": "dtu/hmmgene/hmmgene", "name": "HMMgene 1.1", "maker": "dtu", "family": "dtu/hmmgene", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4745, "dossier_id": "dtu/icerfire/icerfire", "name": "ICERFIRE 1.0", "maker": "dtu", "family": "dtu/icerfire", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4746, "dossier_id": "dtu/immunogenn/immunogenn", "name": "ImmunoGeNN 1.0", "maker": "dtu", "family": "dtu/immunogenn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4747, "dossier_id": "dtu/lipop/lipop", "name": "LipoP 1.0", "maker": "dtu", "family": "dtu/lipop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4748, "dossier_id": "dtu/lyra/lyra", "name": "LYRA 1.0", "maker": "dtu", "family": "dtu/lyra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lymphocyte receptor automated modelling. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4749, "dossier_id": "dtu/mhccluster/mhccluster", "name": "MHCcluster 2.0", "maker": "dtu", "family": "dtu/mhccluster", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MHC clustering based on binding specificity. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4750, "dossier_id": "dtu/mhcmotifdecon/mhcmotifdecon", "name": "MHCMotifDecon 1.3", "maker": "dtu", "family": "dtu/mhcmotifdecon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4751, "dossier_id": "dtu/mupexi/mupexi", "name": "MuPeXI 1.1", "maker": "dtu", "family": "dtu/mupexi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4752, "dossier_id": "dtu/netallergen/netallergen", "name": "NetAllergen 1.0", "maker": "dtu", "family": "dtu/netallergen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Protein allergenicity prediction. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4753, "dossier_id": "dtu/netaspgene/netaspgene", "name": "NetAspGene 1.0", "maker": "dtu", "family": "dtu/netaspgene", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Intron splice sites in Aspergillus DNA. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4754, "dossier_id": "dtu/netbolaiipan/netbolaiipan", "name": "NetBoLAIIpan 1.0", "maker": "dtu", "family": "dtu/netbolaiipan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4755, "dossier_id": "dtu/netbolapan/netbolapan", "name": "NetBoLApan 1.0", "maker": "dtu", "family": "dtu/netbolapan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4756, "dossier_id": "dtu/netcglyc/netcglyc", "name": "NetCGlyc 1.0", "maker": "dtu", "family": "dtu/netcglyc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "C-mannosylation sites in mammalian proteins. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4757, "dossier_id": "dtu/netchop/netchop", "name": "NetChop 3.1", "maker": "dtu", "family": "dtu/netchop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Proteasomal cleavage of MHC ligands. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4758, "dossier_id": "dtu/netctl/netctl", "name": "NetCTL 1.2", "maker": "dtu", "family": "dtu/netctl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4759, "dossier_id": "dtu/netctlpan/netctlpan", "name": "NetCTLpan 1.1", "maker": "dtu", "family": "dtu/netctlpan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4760, "dossier_id": "dtu/netgene2/netgene2", "name": "NetGene2 2.42", "maker": "dtu", "family": "dtu/netgene2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4761, "dossier_id": "dtu/netgpi/netgpi", "name": "NetGPI 1.1", "maker": "dtu", "family": "dtu/netgpi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model NetGPI, current release 1.1, for GPI-anchor prediction. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4762, "dossier_id": "dtu/neth2pan/neth2pan", "name": "NetH2pan 1.0", "maker": "dtu", "family": "dtu/neth2pan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4763, "dossier_id": "dtu/netmhc/netmhc", "name": "NetMHC 4.0", "maker": "dtu", "family": "dtu/netmhc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4764, "dossier_id": "dtu/netmhccons/netmhccons", "name": "NetMHCcons 1.1", "maker": "dtu", "family": "dtu/netmhccons", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4765, "dossier_id": "dtu/netmhcii/netmhcii", "name": "NetMHCII 2.3", "maker": "dtu", "family": "dtu/netmhcii", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4766, "dossier_id": "dtu/netmhciipan/netmhciipan", "name": "NetMHCIIpan 4.3", "maker": "dtu", "family": "dtu/netmhciipan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4767, "dossier_id": "dtu/netmhciiphospan/netmhciiphospan", "name": "NetMHCIIphosPan 1.0", "maker": "dtu", "family": "dtu/netmhciiphospan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4768, "dossier_id": "dtu/netmhcpan/netmhcpan", "name": "NetMHCpan 4.2", "maker": "dtu", "family": "dtu/netmhcpan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4769, "dossier_id": "dtu/netmhcpanexp/netmhcpanexp", "name": "NetMHCpanExp 1.0", "maker": "dtu", "family": "dtu/netmhcpanexp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4770, "dossier_id": "dtu/netmhcphospan/netmhcphospan", "name": "NetMHCphosPan 1.0", "maker": "dtu", "family": "dtu/netmhcphospan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4771, "dossier_id": "dtu/netmhcstab/netmhcstab", "name": "NetMHCstab 1.0", "maker": "dtu", "family": "dtu/netmhcstab", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4772, "dossier_id": "dtu/netmhcstabpan/netmhcstabpan", "name": "NetMHCstabpan 1.0", "maker": "dtu", "family": "dtu/netmhcstabpan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4773, "dossier_id": "dtu/netnes/netnes", "name": "NetNES 1.1", "maker": "dtu", "family": "dtu/netnes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4774, "dossier_id": "dtu/netnglyc/netnglyc", "name": "NetNGlyc 1.0", "maker": "dtu", "family": "dtu/netnglyc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4775, "dossier_id": "dtu/netoglyc/netoglyc", "name": "NetOGlyc 4.0", "maker": "dtu", "family": "dtu/netoglyc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4776, "dossier_id": "dtu/netphos/netphos", "name": "NetPhos 3.1", "maker": "dtu", "family": "dtu/netphos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generic phosphorylation sites in eukaryotic proteins. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4777, "dossier_id": "dtu/netphosbac/netphosbac", "name": "NetPhosBac 1.0", "maker": "dtu", "family": "dtu/netphosbac", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generic phosphorylation sites in bacterial proteins. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4778, "dossier_id": "dtu/netphospan/netphospan", "name": "NetPhospan 1.0", "maker": "dtu", "family": "dtu/netphospan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Phosphorylation prediction using convolutional neural networks. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4779, "dossier_id": "dtu/netphosyeast/netphosyeast", "name": "NetPhosYeast 1.0", "maker": "dtu", "family": "dtu/netphosyeast", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4780, "dossier_id": "dtu/netsolp/netsolp", "name": "NetSolP 1.0", "maker": "dtu", "family": "dtu/netsolp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4781, "dossier_id": "dtu/netstart/netstart", "name": "NetStart 2.0", "maker": "dtu", "family": "dtu/netstart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4782, "dossier_id": "dtu/netsurfp/netsurfp", "name": "NetSurfP 3.0", "maker": "dtu", "family": "dtu/netsurfp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model NetSurfP, current release 3.0, for one-dimensional protein structure features. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4783, "dossier_id": "dtu/nettcr/nettcr", "name": "NetTCR 2.2", "maker": "dtu", "family": "dtu/nettcr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model NetTCR, current release 2.2, for peptide and T-cell receptor binding. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4784, "dossier_id": "dtu/nettepi/nettepi", "name": "NetTepi 1.0", "maker": "dtu", "family": "dtu/nettepi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4785, "dossier_id": "dtu/netturnp/netturnp", "name": "NetTurnP 1.0", "maker": "dtu", "family": "dtu/netturnp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4786, "dossier_id": "dtu/netutr/netutr", "name": "NetUTR 1.0", "maker": "dtu", "family": "dtu/netutr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4787, "dossier_id": "dtu/nnalign/nnalign", "name": "NNAlign 2.1", "maker": "dtu", "family": "dtu/nnalign", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4788, "dossier_id": "dtu/pathogenfinder/pathogenfinder", "name": "PathogenFinder", "maker": "dtu", "family": "dtu/pathogenfinder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4789, "dossier_id": "dtu/phsol/phsol", "name": "pHSol 1.1", "maker": "dtu", "family": "dtu/phsol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4790, "dossier_id": "dtu/pickpocket/pickpocket", "name": "PickPocket 1.1", "maker": "dtu", "family": "dtu/pickpocket", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4791, "dossier_id": "dtu/promoter/promoter", "name": "Promoter 2.0", "maker": "dtu", "family": "dtu/promoter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Transcription start sites in vertebrate DNA. This sentence is the services-index or software-page description, not a paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4792, "dossier_id": "dtu/prop/prop", "name": "ProP 1.0", "maker": "dtu", "family": "dtu/prop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4793, "dossier_id": "dtu/rnammer/rnammer", "name": "RNAmmer 1.2", "maker": "dtu", "family": "dtu/rnammer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4794, "dossier_id": "dtu/secretomep/secretomep", "name": "SecretomeP 2.0", "maker": "dtu", "family": "dtu/secretomep", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4795, "dossier_id": "dtu/signalp/signalp", "name": "SignalP 6.0", "maker": "dtu", "family": "dtu/signalp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4796, "dossier_id": "dtu/signisite/signisite", "name": "SigniSite 2.1", "maker": "dtu", "family": "dtu/signisite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4797, "dossier_id": "dtu/targetp/targetp", "name": "TargetP 2.0", "maker": "dtu", "family": "dtu/targetp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "DTU Health Tech model TargetP, current release 2.0, for N-terminal targeting peptides. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4798, "dossier_id": "dtu/tatp/tatp", "name": "TatP 1.0", "maker": "dtu", "family": "dtu/tatp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4799, "dossier_id": "dtu/tcrpmhcmodels/tcrpmhcmodels", "name": "TCRpMHCmodels 1.0", "maker": "dtu", "family": "dtu/tcrpmhcmodels", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4800, "dossier_id": "dtu/tmhmm/tmhmm", "name": "TMHMM 2.0", "maker": "dtu", "family": "dtu/tmhmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The server predicts transmembrane helices. Its own page says it is outdated and names DeepTMHMM as the replacement.", "summary_intro": "DTU Health Tech model TMHMM 2.0, a deprecated transmembrane-helix predictor that is still online. Status: deprecated. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4801, "dossier_id": "dtu/vardom/vardom", "name": "VarDom 1.0", "maker": "dtu", "family": "dtu/vardom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4802, "dossier_id": "dtu/yinoyang/yinoyang", "name": "YinOYang 1.2", "maker": "dtu", "family": "dtu/yinoyang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4803, "dossier_id": "duesseldorf/helixer/helixer", "name": "Helixer", "maker": "duesseldorf", "family": "duesseldorf/helixer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4804, "dossier_id": "duesseldorf/predmoter/predmoter", "name": "Predmoter", "maker": "duesseldorf", "family": "duesseldorf/predmoter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4805, "dossier_id": "duke/breastsegnet/breastsegnet", "name": "BreastSegNet", "maker": "duke", "family": "duke/breastsegnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4806, "dossier_id": "duke/dukenet/dukenet-1-4", "name": "DukeNet-1.4", "maker": "duke", "family": "duke/dukenet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4807, "dossier_id": "duke/guidedmorph/guidedmorph", "name": "GuidedMorph", "maker": "duke", "family": "duke/guidedmorph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4808, "dossier_id": "duke/legsegnet/legsegnet", "name": "LegSegNet", "maker": "duke", "family": "duke/legsegnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4809, "dossier_id": "duke/lung-ct/dlcsd-md", "name": "DLCSD-mD", "maker": "duke", "family": "duke/lung-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4810, "dossier_id": "duke/lung-ct/luna16-md", "name": "LUNA16-mD", "maker": "duke", "family": "duke/lung-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4811, "dossier_id": "duke/lung-ct/resnet50-sws", "name": "ResNet50-SWS", "maker": "duke", "family": "duke/lung-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4812, "dossier_id": "duke/mri-core/mri-core", "name": "MRI-CORE", "maker": "duke", "family": "duke/mri-core", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4813, "dossier_id": "dundee/1433pred/1433pred", "name": "14-3-3-Pred", "maker": "dundee", "family": "dundee/1433pred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4814, "dossier_id": "dundee/jpred/jpred", "name": "JPred", "maker": "dundee", "family": "dundee/jpred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4815, "dossier_id": "dundee/kinomer/kinomer", "name": "Kinomer", "maker": "dundee", "family": "dundee/kinomer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4816, "dossier_id": "dundee/nod/nod", "name": "NoD", "maker": "dundee", "family": "dundee/nod", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4817, "dossier_id": "dundee/xtal/ob-score", "name": "OB-Score", "maker": "dundee", "family": "dundee/xtal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4818, "dossier_id": "dundee/xtal/parcrys", "name": "ParCrys", "maker": "dundee", "family": "dundee/xtal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4819, "dossier_id": "dundee/xtal/xannpred", "name": "XANNpred", "maker": "dundee", "family": "dundee/xtal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4820, "dossier_id": "durham/mednerf/mednerf", "name": "MedNeRF", "maker": "durham", "family": "durham/mednerf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released MedNeRF. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4821, "dossier_id": "durham/onfire/firenet", "name": "FireNet", "maker": "durham", "family": "durham/onfire", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released FireNet. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4822, "dossier_id": "durham/onfire/inceptionv1-onfire", "name": "InceptionV1-OnFire", "maker": "durham", "family": "durham/onfire", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released InceptionV1-OnFire. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4823, "dossier_id": "durham/onfire/inceptionv3-onfire", "name": "InceptionV3-OnFire", "maker": "durham", "family": "durham/onfire", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released InceptionV3-OnFire. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4824, "dossier_id": "durham/onfire/inceptionv4-onfire", "name": "InceptionV4-OnFire", "maker": "durham", "family": "durham/onfire", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released InceptionV4-OnFire. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4825, "dossier_id": "durham/onfire/nasnet-a-onfire", "name": "NasNet-A-OnFire", "maker": "durham", "family": "durham/onfire", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released NasNet-A-OnFire. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4826, "dossier_id": "durham/onfire/shufflenetv2-onfire", "name": "ShuffleNetV2-OnFire", "maker": "durham", "family": "durham/onfire", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released ShuffleNetV2-OnFire. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4827, "dossier_id": "durham/raindrop/alexnet-30-2", "name": "AlexNet-30^2", "maker": "durham", "family": "durham/raindrop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released AlexNet-30^2. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4828, "dossier_id": "durham/unleashing-transformers/unleashing-transformers", "name": "Unleashing Transformers", "maker": "durham", "family": "durham/unleashing-transformers", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Durham University released Unleashing Transformers. Status active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4829, "dossier_id": "ecnu/artaug/artaug-lora-flux-1-dev-v1", "name": "ArtAug LoRA for FLUX.1-dev", "maker": "ecnu", "family": "ecnu/artaug", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4830, "dossier_id": "ecnu/educhat/educhat-sft-002-14b-qwen1-5", "name": "EduChat 1.0 14B (Qwen1.5)", "maker": "ecnu", "family": "ecnu/educhat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4831, "dossier_id": "ecnu/exvideo/exvideo-cogvideox-lora-129f-v1", "name": "ExVideo-CogVideoX-LoRA-129f-v1", "maker": "ecnu", "family": "ecnu/exvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4832, "dossier_id": "ecnu/exvideo/exvideo-svd-128f-v1", "name": "ExVideo-SVD-128f-v1", "maker": "ecnu", "family": "ecnu/exvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4833, "dossier_id": "ecnu/sea/sea-a", "name": "SEA-A", "maker": "ecnu", "family": "ecnu/sea", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says SEA-A assesses consistency between paper content and a review and produces a mismatch score.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4834, "dossier_id": "ecust/aipolym/chat-aipolym", "name": "Chat AIPolym", "maker": "ecust", "family": "ecust/aipolym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4835, "dossier_id": "edinburgh/eurofilter/eurofilter-v1", "name": "EuroFilter v1", "maker": "edinburgh", "family": "edinburgh/eurofilter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4836, "dossier_id": "edinburgh/euromoe/euromoe-2-6b-a0-6b", "name": "EuroMoE-2.6B-A0.6B", "maker": "edinburgh", "family": "edinburgh/euromoe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4837, "dossier_id": "edinburgh/euromoe/euromoe-2-6b-a0-6b-instruct", "name": "EuroMoE-2.6B-A0.6B-Instruct", "maker": "edinburgh", "family": "edinburgh/euromoe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4838, "dossier_id": "edinburgh/wmt/wmt16", "name": "Edinburgh WMT16 NMT", "maker": "edinburgh", "family": "edinburgh/wmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4839, "dossier_id": "edinburgh/wmt/wmt17", "name": "Edinburgh WMT17 NMT", "maker": "edinburgh", "family": "edinburgh/wmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4840, "dossier_id": "edinburgh/wmt/wmt19", "name": "Edinburgh WMT19 NMT", "maker": "edinburgh", "family": "edinburgh/wmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4841, "dossier_id": "edinburgh/wmt/wmt20", "name": "Edinburgh WMT20 NMT", "maker": "edinburgh", "family": "edinburgh/wmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4842, "dossier_id": "elai/custom/animated-mascot", "name": "Animated Mascot", "maker": "elai", "family": "elai/custom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4843, "dossier_id": "elai/custom/photo-avatar", "name": "Photo Avatar", "maker": "elai", "family": "elai/custom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4844, "dossier_id": "elai/custom/premium-avatars", "name": "Premium Avatars", "maker": "elai", "family": "elai/custom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4845, "dossier_id": "elai/custom/selfie-avatar", "name": "Selfie Avatar", "maker": "elai", "family": "elai/custom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4846, "dossier_id": "elai/custom/studio-avatar", "name": "Studio Avatar", "maker": "elai", "family": "elai/custom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4847, "dossier_id": "elai/stock/ali", "name": "Ali", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4848, "dossier_id": "elai/stock/alma", "name": "Alma", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4849, "dossier_id": "elai/stock/amanda", "name": "Amanda", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4850, "dossier_id": "elai/stock/amber", "name": "Amber", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4851, "dossier_id": "elai/stock/amira", "name": "Amira", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4852, "dossier_id": "elai/stock/aniqa", "name": "Aniqa", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4853, "dossier_id": "elai/stock/aspen", "name": "Aspen", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4854, "dossier_id": "elai/stock/carlos", "name": "Carlos", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4855, "dossier_id": "elai/stock/chad", "name": "Chad", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4856, "dossier_id": "elai/stock/cody", "name": "Cody", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4857, "dossier_id": "elai/stock/enzo", "name": "Enzo", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4858, "dossier_id": "elai/stock/ethan", "name": "Ethan", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4859, "dossier_id": "elai/stock/gia", "name": "Gia", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4860, "dossier_id": "elai/stock/jade", "name": "Jade", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4861, "dossier_id": "elai/stock/jameson", "name": "Jameson", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4862, "dossier_id": "elai/stock/kamal", "name": "Kamal", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4863, "dossier_id": "elai/stock/kevin", "name": "Kevin", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4864, "dossier_id": "elai/stock/kira", "name": "Kira", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4865, "dossier_id": "elai/stock/lei", "name": "Lei", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4866, "dossier_id": "elai/stock/lucia", "name": "Lucia", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4867, "dossier_id": "elai/stock/malik", "name": "Malik", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4868, "dossier_id": "elai/stock/margaret", "name": "Margaret", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4869, "dossier_id": "elai/stock/max", "name": "Max", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4870, "dossier_id": "elai/stock/megan", "name": "Megan", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4871, "dossier_id": "elai/stock/nancy", "name": "Nancy", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4872, "dossier_id": "elai/stock/naomi", "name": "Naomi", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4873, "dossier_id": "elai/stock/nelson", "name": "Nelson", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4874, "dossier_id": "elai/stock/noah", "name": "Noah", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4875, "dossier_id": "elai/stock/olivia", "name": "Olivia", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4876, "dossier_id": "elai/stock/rose", "name": "Rose", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4877, "dossier_id": "elai/stock/skye", "name": "Skye", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4878, "dossier_id": "elai/stock/sofie", "name": "Sofie", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4879, "dossier_id": "elai/stock/taylor", "name": "Taylor", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4880, "dossier_id": "elai/stock/terry", "name": "Terry", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4881, "dossier_id": "elai/stock/tyler", "name": "Tyler", "maker": "elai", "family": "elai/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4882, "dossier_id": "eleutherai/pile-t5/pile-t5-base", "name": "Pile-T5 Base", "maker": "eleutherai", "family": "eleutherai/pile-t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4883, "dossier_id": "eleutherai/pile-t5/pile-t5-large", "name": "Pile-T5 Large", "maker": "eleutherai", "family": "eleutherai/pile-t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4884, "dossier_id": "eleutherai/pile-t5/pile-t5-xl", "name": "Pile-T5 XL", "maker": "eleutherai", "family": "eleutherai/pile-t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4885, "dossier_id": "eleutherai/pile-t5/pile-t5-xxl", "name": "Pile-T5 XXL", "maker": "eleutherai", "family": "eleutherai/pile-t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4886, "dossier_id": "elevenlabs/dubbing/dubbing-v1", "name": "Dubbing v1", "maker": "elevenlabs", "family": "elevenlabs/dubbing", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4887, "dossier_id": "elevenlabs/dubbing/dubbing-v2", "name": "Dubbing v2", "maker": "elevenlabs", "family": "elevenlabs/dubbing", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4888, "dossier_id": "elevenlabs/eleven/eleven-flash-v2", "name": "Eleven Flash v2", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4889, "dossier_id": "elevenlabs/eleven/eleven-flash-v2-5", "name": "Eleven Flash v2.5", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4890, "dossier_id": "elevenlabs/eleven/eleven-monolingual-v1", "name": "Eleven Monolingual v1", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4891, "dossier_id": "elevenlabs/eleven/eleven-multilingual-v1", "name": "Eleven Multilingual v1", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4892, "dossier_id": "elevenlabs/eleven/eleven-multilingual-v2", "name": "Eleven Multilingual v2", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4893, "dossier_id": "elevenlabs/eleven/eleven-turbo-v2", "name": "Eleven Turbo v2", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4894, "dossier_id": "elevenlabs/eleven/eleven-turbo-v2-5", "name": "Eleven Turbo v2.5", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4895, "dossier_id": "elevenlabs/eleven/eleven-v3", "name": "Eleven v3", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4896, "dossier_id": "elevenlabs/eleven/eleven-v3-conversational", "name": "Eleven v3 Conversational", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4897, "dossier_id": "elevenlabs/eleven/eleven-v4", "name": "Eleven v4", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4898, "dossier_id": "elevenlabs/eleven/eleven-v4-turbo", "name": "Eleven v4 Turbo", "maker": "elevenlabs", "family": "elevenlabs/eleven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4899, "dossier_id": "elevenlabs/music/music-v1", "name": "Eleven Music v1", "maker": "elevenlabs", "family": "elevenlabs/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4900, "dossier_id": "elevenlabs/music/music-v2", "name": "Eleven Music v2", "maker": "elevenlabs", "family": "elevenlabs/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4901, "dossier_id": "elevenlabs/music/music-v2-5", "name": "Eleven Music v2.5", "maker": "elevenlabs", "family": "elevenlabs/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4902, "dossier_id": "elevenlabs/scribe/scribe-v1", "name": "Scribe v1", "maker": "elevenlabs", "family": "elevenlabs/scribe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4903, "dossier_id": "elevenlabs/scribe/scribe-v2", "name": "Scribe v2", "maker": "elevenlabs", "family": "elevenlabs/scribe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.22, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4904, "dossier_id": "elevenlabs/scribe/scribe-v2-medical", "name": "Scribe v2 Medical", "maker": "elevenlabs", "family": "elevenlabs/scribe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.22, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4905, "dossier_id": "elevenlabs/scribe/scribe-v2-realtime", "name": "Scribe v2 Realtime", "maker": "elevenlabs", "family": "elevenlabs/scribe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.39, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4906, "dossier_id": "elevenlabs/sound-effects/eleven-text-to-sound-v2", "name": "Eleven Text to Sound v2", "maker": "elevenlabs", "family": "elevenlabs/sound-effects", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4907, "dossier_id": "elevenlabs/sts/eleven-english-sts-v1", "name": "Eleven English STS v1", "maker": "elevenlabs", "family": "elevenlabs/sts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4908, "dossier_id": "elevenlabs/sts/eleven-english-sts-v2", "name": "Eleven English STS v2", "maker": "elevenlabs", "family": "elevenlabs/sts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4909, "dossier_id": "elevenlabs/sts/eleven-multilingual-sts-v2", "name": "Eleven Multilingual STS v2", "maker": "elevenlabs", "family": "elevenlabs/sts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4910, "dossier_id": "elevenlabs/turn/turn-v2", "name": "Turn v2", "maker": "elevenlabs", "family": "elevenlabs/turn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4911, "dossier_id": "elevenlabs/turn/turn-v3", "name": "Turn v3", "maker": "elevenlabs", "family": "elevenlabs/turn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4912, "dossier_id": "elevenlabs/voice-design/eleven-multilingual-ttv-v2", "name": "Eleven Multilingual Text to Voice v2", "maker": "elevenlabs", "family": "elevenlabs/voice-design", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4913, "dossier_id": "elevenlabs/voice-design/eleven-ttv-v3", "name": "Eleven Text to Voice v3", "maker": "elevenlabs", "family": "elevenlabs/voice-design", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4914, "dossier_id": "elyza/brain/elyza-brain", "name": "ELYZA Brain", "maker": "elyza", "family": "elyza/brain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4915, "dossier_id": "elyza/japanese-llama-2/elyza-japanese-llama-2-70b", "name": "ELYZA-japanese-Llama-2-70b", "maker": "elyza", "family": "elyza/japanese-llama-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4916, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4917, "dossier_id": "elyza/llama-3-jp/llama-3-elyza-jp-120b", "name": "Llama-3-ELYZA-JP-120B", "maker": "elyza", "family": "elyza/llama-3-jp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4918, "dossier_id": "elyza/llama-3-jp/llama-3-elyza-jp-70b", "name": "Llama-3-ELYZA-JP-70B", "maker": "elyza", "family": "elyza/llama-3-jp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4919, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4920, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4921, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4922, "dossier_id": "ens/addressee/addressee", "name": "Addressee", "maker": "ens", "family": "ens/addressee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4923, "dossier_id": "ens/babyhubert/babyhubert", "name": "BabyHuBERT", "maker": "ens", "family": "ens/babyhubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4924, "dossier_id": "ens/hubert/hubert-base-mmsulab", "name": "HuBERT MMS-ulab", "maker": "ens", "family": "ens/hubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4925, "dossier_id": "ens/hubert/hubert-base-vp20", "name": "HuBERT VP-20", "maker": "ens", "family": "ens/hubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4926, "dossier_id": "ens/hubert/hubert-phoneme-classification", "name": "HuBERT phoneme classification", "maker": "ens", "family": "ens/hubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4927, "dossier_id": "ens/maubert/maubert-feat", "name": "MauBERT-feat", "maker": "ens", "family": "ens/maubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4928, "dossier_id": "ens/maubert/maubert-phone", "name": "MauBERT-phone", "maker": "ens", "family": "ens/maubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4929, "dossier_id": "ens/maubert/maubert-quantisers", "name": "MauBERT quantisers", "maker": "ens", "family": "ens/maubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4930, "dossier_id": "ens/spidr/spidr-mmsulab", "name": "SpidR MMS-ulab", "maker": "ens", "family": "ens/spidr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4931, "dossier_id": "ens/spidr/spidr-vp20", "name": "SpidR VP-20", "maker": "ens", "family": "ens/spidr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4932, "dossier_id": "ens/vtc/vtc-2", "name": "VTC 2", "maker": "ens", "family": "ens/vtc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4933, "dossier_id": "epfl/4m/4m-21-b", "name": "4M-21 B", "maker": "epfl", "family": "epfl/4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4934, "dossier_id": "epfl/4m/4m-21-l", "name": "4M-21 L", "maker": "epfl", "family": "epfl/4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4935, "dossier_id": "epfl/4m/4m-21-xl", "name": "4M-21 XL", "maker": "epfl", "family": "epfl/4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4936, "dossier_id": "epfl/4m/4m-7-b", "name": "4M-7 B", "maker": "epfl", "family": "epfl/4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4937, "dossier_id": "epfl/4m/4m-7-l", "name": "4M-7 L", "maker": "epfl", "family": "epfl/4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4938, "dossier_id": "epfl/4m/4m-7-xl", "name": "4M-7 XL", "maker": "epfl", "family": "epfl/4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4939, "dossier_id": "epfl/apertus/apertus-2-0", "name": "Apertus 2.0", "maker": "epfl", "family": "epfl/apertus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The roadmap describes a larger fully open model with a fairer vocabulary. It is not usable yet.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4940, "dossier_id": "epfl/apertus/apertus-v1-1-0-5b", "name": "Apertus v1.1 0.5B", "maker": "epfl", "family": "epfl/apertus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4941, "dossier_id": "epfl/apertus/apertus-v1-1-1-5b", "name": "Apertus v1.1 1.5B", "maker": "epfl", "family": "epfl/apertus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4942, "dossier_id": "epfl/apertus/apertus-v1-1-4b", "name": "Apertus v1.1 4B", "maker": "epfl", "family": "epfl/apertus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4943, "dossier_id": "epfl/flexar/flexar-113m-t2i", "name": "FlexAR 113M T2I", "maker": "epfl", "family": "epfl/flexar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4944, "dossier_id": "epfl/flexar/flexar-1b-c2i", "name": "FlexAR 1B C2I", "maker": "epfl", "family": "epfl/flexar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4945, "dossier_id": "epfl/flexar/flexar-1b-t2i", "name": "FlexAR 1B T2I", "maker": "epfl", "family": "epfl/flexar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4946, "dossier_id": "epfl/flexar/flexar-382m-t2i", "name": "FlexAR 382M T2I", "maker": "epfl", "family": "epfl/flexar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4947, "dossier_id": "epfl/flexar/flexar-3b-t2i", "name": "FlexAR 3B T2I", "maker": "epfl", "family": "epfl/flexar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4948, "dossier_id": "epfl/flexar/gridar-3b-t2i", "name": "GridAR 3B T2I", "maker": "epfl", "family": "epfl/flexar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4949, "dossier_id": "epfl/flextok/flextok-d12-d12-in1k", "name": "FlexTok d12-d12 IN1K", "maker": "epfl", "family": "epfl/flextok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4950, "dossier_id": "epfl/flextok/flextok-d18-d18-in1k", "name": "FlexTok d18-d18 IN1K", "maker": "epfl", "family": "epfl/flextok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4951, "dossier_id": "epfl/flextok/flextok-d18-d28-dfn", "name": "FlexTok d18-d28 DFN", "maker": "epfl", "family": "epfl/flextok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4952, "dossier_id": "epfl/flextok/flextok-d18-d28-in1k", "name": "FlexTok d18-d28 IN1K", "maker": "epfl", "family": "epfl/flextok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4953, "dossier_id": "epfl/flextok/flextok-vae", "name": "FlexTok VAE", "maker": "epfl", "family": "epfl/flextok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "They compress images before FlexTok's 1D resampler. They are not generative products on their own.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4954, "dossier_id": "epfl/meditron/meditron3-70b", "name": "Meditron 3 70B", "maker": "epfl", "family": "epfl/meditron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4955, "dossier_id": "epfl/meditron/meditron3-8b", "name": "Meditron 3 8B", "maker": "epfl", "family": "epfl/meditron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4956, "dossier_id": "epfl/meditronfo/apertus-70b-meditronfo", "name": "Apertus 70B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4957, "dossier_id": "epfl/meditronfo/apertus-8b-meditronfo", "name": "Apertus 8B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4958, "dossier_id": "epfl/meditronfo/eurollm-22b-meditronfo", "name": "EuroLLM 22B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4959, "dossier_id": "epfl/meditronfo/eurollm-9b-meditronfo", "name": "EuroLLM 9B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4960, "dossier_id": "epfl/meditronfo/gemma-3-27b-meditronfo", "name": "Gemma 3 27B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4961, "dossier_id": "epfl/meditronfo/olmo-2-32b-meditronfo", "name": "OLMo 2 32B MeditronFO", "maker": "epfl", "family": "epfl/meditronfo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4962, "dossier_id": "epfl/svi/svi-dance", "name": "SVI-Dance", "maker": "epfl", "family": "epfl/svi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4963, "dossier_id": "epfl/svi/svi-film", "name": "SVI-Film", "maker": "epfl", "family": "epfl/svi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4964, "dossier_id": "epfl/svi/svi-film-transition", "name": "SVI-Film Transition", "maker": "epfl", "family": "epfl/svi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Film variant trained for more scene transitions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4965, "dossier_id": "epfl/svi/svi-shot", "name": "SVI-Shot", "maker": "epfl", "family": "epfl/svi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4966, "dossier_id": "epfl/svi/svi-talk", "name": "SVI-Talk", "maker": "epfl", "family": "epfl/svi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Talking-head video from an image and audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4967, "dossier_id": "epfl/svi/svi-tom", "name": "SVI-Tom", "maker": "epfl", "family": "epfl/svi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4968, "dossier_id": "epfl/videoflextok/videoflextok-d18-d18-k600", "name": "VideoFlexTok d18-d18 K600", "maker": "epfl", "family": "epfl/videoflextok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same job as the larger VideoFlexTok: flexible-length coarse-to-fine video tokens, at 128x128.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4969, "dossier_id": "epfl/videoflextok/videoflextok-d18-d28", "name": "VideoFlexTok d18-d28", "maker": "epfl", "family": "epfl/videoflextok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4970, "dossier_id": "eth/apertus/apertus-2-0", "name": "Apertus 2.0", "maker": "eth", "family": "eth/apertus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Apertus 2.0 (apertus-2-0). Status: announced. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4971, "dossier_id": "eth/forpt/forpt", "name": "ForPT", "maker": "eth", "family": "eth/forpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model ForPT (forpt). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4972, "dossier_id": "eth/geocalib/geocalib", "name": "GeoCalib", "maker": "eth", "family": "eth/geocalib", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Estimate camera intrinsics from one image, with a geometric refinement step rather than a pure regression head.", "summary_intro": "ETH Zurich model GeoCalib (geocalib). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4973, "dossier_id": "eth/gluestick/gluestick", "name": "GlueStick", "maker": "eth", "family": "eth/gluestick", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Match points and line segments together, including in low-texture areas where point-only matchers fail.", "summary_intro": "ETH Zurich model GlueStick (gluestick). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4974, "dossier_id": "eth/lightglue/lightglue-disk", "name": "LightGlue DISK", "maker": "eth", "family": "eth/lightglue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same matching job as the SuperPoint LightGlue, with a license the card says allows commercial use.", "summary_intro": "ETH Zurich model LightGlue DISK (lightglue-disk). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4975, "dossier_id": "eth/lightglue/lightglue-superpoint", "name": "LightGlue SuperPoint", "maker": "eth", "family": "eth/lightglue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Match sparse keypoints between two images for pose and homography, faster than SuperGlue on easy pairs.", "summary_intro": "ETH Zurich model LightGlue SuperPoint (lightglue-superpoint). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4976, "dossier_id": "eth/litept/litept", "name": "LitePT", "maker": "eth", "family": "eth/litept", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model LitePT (litept). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4977, "dossier_id": "eth/marigold/marigold-depth", "name": "Marigold Depth", "maker": "eth", "family": "eth/marigold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Marigold Depth (marigold-depth). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4978, "dossier_id": "eth/marigold/marigold-depth-hr", "name": "Marigold Depth HR", "maker": "eth", "family": "eth/marigold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Marigold Depth HR (marigold-depth-hr). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4979, "dossier_id": "eth/marigold/marigold-iid-appearance", "name": "Marigold IID Appearance", "maker": "eth", "family": "eth/marigold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Marigold IID Appearance (marigold-iid-appearance). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4980, "dossier_id": "eth/marigold/marigold-iid-lighting", "name": "Marigold IID Lighting", "maker": "eth", "family": "eth/marigold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Marigold IID Lighting (marigold-iid-lighting). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4981, "dossier_id": "eth/marigold/marigold-normals", "name": "Marigold Normals", "maker": "eth", "family": "eth/marigold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Monocular surface normals. Outputs 3D unit vectors in the screen-space camera frame.", "summary_intro": "ETH Zurich model Marigold Normals (marigold-normals). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4982, "dossier_id": "eth/pager/pager", "name": "PaGeR", "maker": "eth", "family": "eth/pager", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model PaGeR (pager). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4983, "dossier_id": "eth/pager/pager-metric-depth", "name": "PaGeR Metric Depth", "maker": "eth", "family": "eth/pager", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Depth-only checkpoint. Metric depth in metres from one head, with no separate scale head and no indoor/outdoor routing.", "summary_intro": "ETH Zurich model PaGeR Metric Depth (pager-metric-depth). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4984, "dossier_id": "eth/pager/pager-normals", "name": "PaGeR Normals", "maker": "eth", "family": "eth/pager", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Normals-only checkpoint. Dense unit normals in the panorama world frame.", "summary_intro": "ETH Zurich model PaGeR Normals (pager-normals). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4985, "dossier_id": "eth/poseidon/poseidon-b", "name": "Poseidon-B", "maker": "eth", "family": "eth/poseidon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrained operator model for fluid and related PDE systems, meant to be finetuned on downstream equation families.", "summary_intro": "ETH Zurich model Poseidon-B (poseidon-b). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4986, "dossier_id": "eth/poseidon/poseidon-l", "name": "Poseidon-L", "maker": "eth", "family": "eth/poseidon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrained operator model for fluid and related PDE systems, meant to be finetuned on downstream equation families.", "summary_intro": "ETH Zurich model Poseidon-L (poseidon-l). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4987, "dossier_id": "eth/poseidon/poseidon-t", "name": "Poseidon-T", "maker": "eth", "family": "eth/poseidon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pretrained operator model for fluid and related PDE systems, meant to be finetuned on downstream equation families.", "summary_intro": "ETH Zurich model Poseidon-T (poseidon-t). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4988, "dossier_id": "eth/rollingdepth/rollingdepth", "name": "RollingDepth", "maker": "eth", "family": "eth/rollingdepth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model RollingDepth (rollingdepth). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4989, "dossier_id": "eth/stereospace/stereospace", "name": "StereoSpace", "maker": "eth", "family": "eth/stereospace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Synthesize a second view from a single image so the pair has controllable stereo geometry, without a separate depth stage.", "summary_intro": "ETH Zurich model StereoSpace (stereospace). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4990, "dossier_id": "eth/thera/thera-edsr-air", "name": "Thera EDSR-baseline Air", "maker": "eth", "family": "eth/thera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Thera EDSR-baseline Air (thera-edsr-air). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4991, "dossier_id": "eth/thera/thera-edsr-plus", "name": "Thera EDSR-baseline Plus", "maker": "eth", "family": "eth/thera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Thera EDSR-baseline Plus (thera-edsr-plus). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4992, "dossier_id": "eth/thera/thera-edsr-pro", "name": "Thera EDSR-baseline Pro", "maker": "eth", "family": "eth/thera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Thera EDSR-baseline Pro (thera-edsr-pro). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4993, "dossier_id": "eth/thera/thera-rdn-air", "name": "Thera RDN Air", "maker": "eth", "family": "eth/thera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Thera RDN Air (thera-rdn-air). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4994, "dossier_id": "eth/thera/thera-rdn-plus", "name": "Thera RDN Plus", "maker": "eth", "family": "eth/thera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Thera RDN Plus (thera-rdn-plus). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4995, "dossier_id": "eth/thera/thera-rdn-pro", "name": "Thera RDN Pro", "maker": "eth", "family": "eth/thera", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ETH Zurich model Thera RDN Pro (thera-rdn-pro). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4996, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4997, "dossier_id": "evolutionaryscale/esmfold2/esmfold2", "name": "ESMFold2", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmfold2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4998, "dossier_id": "evolutionaryscale/esmfold2/esmfold2-fast", "name": "ESMFold2-Fast", "maker": "evolutionaryscale", "family": "evolutionaryscale/esmfold2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 4999, "dossier_id": "fau/dax/dax-resnet18", "name": "DAX ResNet-18", "maker": "fau", "family": "fau/dax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5000, "dossier_id": "fau/dax/dax-resnet50", "name": "DAX ResNet-50", "maker": "fau", "family": "fau/dax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5001, "dossier_id": "fau/dax/dax-vit-s-16", "name": "DAX ViT-S/16", "maker": "fau", "family": "fau/dax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5002, "dossier_id": "fau/dax/dax-vit-s-8", "name": "DAX ViT-S/8", "maker": "fau", "family": "fau/dax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5003, "dossier_id": "fau/dax/dax-vit-t-16", "name": "DAX ViT-T/16", "maker": "fau", "family": "fau/dax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5004, "dossier_id": "fau/dax/dax-vit-t-8", "name": "DAX ViT-T/8", "maker": "fau", "family": "fau/dax", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5005, "dossier_id": "fau/deepfilternet/deepfilternet", "name": "DeepFilterNet", "maker": "fau", "family": "fau/deepfilternet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5006, "dossier_id": "fau/deepfilternet/deepfilternet2", "name": "DeepFilterNet2", "maker": "fau", "family": "fau/deepfilternet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5007, "dossier_id": "fau/deepfilternet/deepfilternet3", "name": "DeepFilterNet3", "maker": "fau", "family": "fau/deepfilternet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5008, "dossier_id": "fau/find-net/find-net", "name": "FIND-Net", "maker": "fau", "family": "fau/find-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5009, "dossier_id": "fish-audio/drama/drama-3", "name": "Fish Audio Drama 3", "maker": "fish-audio", "family": "fish-audio/drama", "context_length": null, "prompt_price_per_m": 50.0, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5010, "dossier_id": "fish-audio/fish-agent/fish-agent-v0-1-3b", "name": "Fish Agent v0.1 3B", "maker": "fish-audio", "family": "fish-audio/fish-agent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5011, "dossier_id": "fish-audio/fish-speech/fish-speech-1", "name": "Fish Speech 1", "maker": "fish-audio", "family": "fish-audio/fish-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5012, "dossier_id": "fish-audio/fish-speech/fish-speech-1-2", "name": "Fish Speech 1.2", "maker": "fish-audio", "family": "fish-audio/fish-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5013, "dossier_id": "fish-audio/fish-speech/fish-speech-1-4", "name": "Fish Speech 1.4", "maker": "fish-audio", "family": "fish-audio/fish-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5014, "dossier_id": "fish-audio/fish-speech/fish-speech-1-5", "name": "Fish Speech 1.5", "maker": "fish-audio", "family": "fish-audio/fish-speech", "context_length": null, "prompt_price_per_m": 15.0, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5015, "dossier_id": "fish-audio/fish-speech/fish-speech-1-6", "name": "Fish Speech 1.6", "maker": "fish-audio", "family": "fish-audio/fish-speech", "context_length": null, "prompt_price_per_m": 15.0, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5016, "dossier_id": "fish-audio/s1/s1", "name": "Fish Audio S1", "maker": "fish-audio", "family": "fish-audio/s1", "context_length": null, "prompt_price_per_m": 15.0, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Fish Audio's Fish Audio S1 is a deprecated model in the s1 family. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5017, "dossier_id": "fish-audio/s1/s1-mini", "name": "Fish Audio S1-mini", "maker": "fish-audio", "family": "fish-audio/s1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5018, "dossier_id": "fish-audio/s2/s2-1-pro", "name": "Fish Audio S2.1-Pro", "maker": "fish-audio", "family": "fish-audio/s2", "context_length": null, "prompt_price_per_m": 15.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5019, "dossier_id": "fish-audio/s2/s2-pro", "name": "Fish Audio S2-Pro", "maker": "fish-audio", "family": "fish-audio/s2", "context_length": null, "prompt_price_per_m": 15.0, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5020, "dossier_id": "fish-audio/transcribe/transcribe-1", "name": "Transcribe 1", "maker": "fish-audio", "family": "fish-audio/transcribe", "context_length": null, "prompt_price_per_m": 0.36, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Fish Audio's Transcribe 1 is a active model in the transcribe family. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5021, "dossier_id": "fish-audio/transcribe/transcribe-1-pro", "name": "Transcribe 1 Pro", "maker": "fish-audio", "family": "fish-audio/transcribe", "context_length": null, "prompt_price_per_m": 0.36, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Fish Audio's Transcribe 1 Pro is a active model in the transcribe family. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5022, "dossier_id": "fish-audio/voice-design/voice-design-1", "name": "Voice Design 1", "maker": "fish-audio", "family": "fish-audio/voice-design", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5023, "dossier_id": "fiu/bindscreen/bindscreen-finetuning-dude", "name": "BindScreen Finetuning DUDE", "maker": "fiu", "family": "fiu/bindscreen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5024, "dossier_id": "fiu/bindscreen/bindscreen-finetuning-lit-pcba", "name": "BindScreen Finetuning LIT-PCBA", "maker": "fiu", "family": "fiu/bindscreen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5025, "dossier_id": "fiu/bindscreen/bindscreen-frozen-dude", "name": "BindScreen Frozen DUDE", "maker": "fiu", "family": "fiu/bindscreen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5026, "dossier_id": "fiu/bindscreen/bindscreen-frozen-lit-pcba", "name": "BindScreen Frozen LIT-PCBA", "maker": "fiu", "family": "fiu/bindscreen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5027, "dossier_id": "fiu/dom-formula-assignment/dom-formula-assignment", "name": "DOM formula assignment", "maker": "fiu", "family": "fiu/dom-formula-assignment", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5028, "dossier_id": "fiu/neuroclr/neuroclr", "name": "NeuroCLR", "maker": "fiu", "family": "fiu/neuroclr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5029, "dossier_id": "fiu/protenrich/protenrich-ankh3", "name": "ProtEnrich - Ankh 3 XL", "maker": "fiu", "family": "fiu/protenrich", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5030, "dossier_id": "fiu/protenrich/protenrich-carp-640m", "name": "ProtEnrich CARP 640M", "maker": "fiu", "family": "fiu/protenrich", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5031, "dossier_id": "fiu/protenrich/protenrich-esm1b", "name": "ProtEnrich ESM1b", "maker": "fiu", "family": "fiu/protenrich", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5032, "dossier_id": "fiu/protenrich/protenrich-esm2-t36", "name": "ProtEnrich ESM2 T36", "maker": "fiu", "family": "fiu/protenrich", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5033, "dossier_id": "fiu/protenrich/protenrich-esmc-600m", "name": "ProtEnrich ESM Cambrian 600M", "maker": "fiu", "family": "fiu/protenrich", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5034, "dossier_id": "fiu/protenrich/protenrich-progen2", "name": "ProtEnrich ProGen2", "maker": "fiu", "family": "fiu/protenrich", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5035, "dossier_id": "fiu/protenrich/protenrich-protbert", "name": "ProtEnrich ProtBERT", "maker": "fiu", "family": "fiu/protenrich", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5036, "dossier_id": "fiu/protenrich/protenrich-prott5", "name": "ProtEnrich ProtT5", "maker": "fiu", "family": "fiu/protenrich", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5037, "dossier_id": "fiu/proteorift/proteorift", "name": "ProteoRift", "maker": "fiu", "family": "fiu/proteorift", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5038, "dossier_id": "fiu/specollate/specollate", "name": "SpeCollate", "maker": "fiu", "family": "fiu/specollate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5039, "dossier_id": "fiu/titan-bbb/titan-bbb", "name": "TITAN-BBB", "maker": "fiu", "family": "fiu/titan-bbb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5040, "dossier_id": "florence/emovoca/de-sd", "name": "EmoVOCA DE-SD", "maker": "florence", "family": "florence/emovoca", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5041, "dossier_id": "florence/emovoca/es2d", "name": "EmoVOCA ES2D", "maker": "florence", "family": "florence/emovoca", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5042, "dossier_id": "florence/emovoca/es2l", "name": "EmoVOCA ES2L", "maker": "florence", "family": "florence/emovoca", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5043, "dossier_id": "florence/scantalk/scantalk", "name": "ScanTalk", "maker": "florence", "family": "florence/scantalk", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5044, "dossier_id": "florence/tape/tape", "name": "TAPE", "maker": "florence", "family": "florence/tape", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5045, "dossier_id": "florida-atlantic/molecular-transformer-embeddings/molecular-transformer-embeddings", "name": "Molecular Transformer Embeddings", "maker": "florida-atlantic", "family": "florida-atlantic/molecular-transformer-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5046, "dossier_id": "freepik/classic/classic", "name": "Classic", "maker": "freepik", "family": "freepik/classic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The model table says Classic is the reliable general generator when the user does not care about a specific style.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5047, "dossier_id": "freepik/classic/classic-fast", "name": "Classic Fast", "maker": "freepik", "family": "freepik/classic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5048, "dossier_id": "freepik/f-lite/f-lite", "name": "F Lite", "maker": "freepik", "family": "freepik/f-lite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5049, "dossier_id": "freepik/f-lite/f-lite-7b", "name": "F Lite 7B", "maker": "freepik", "family": "freepik/f-lite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5050, "dossier_id": "freepik/f-lite/f-lite-texture", "name": "F Lite Texture", "maker": "freepik", "family": "freepik/f-lite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5051, "dossier_id": "freepik/flux-lite/flux-1-lite", "name": "Flux.1 Lite", "maker": "freepik", "family": "freepik/flux-lite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5052, "dossier_id": "freepik/magnific/creative", "name": "Magnific Upscaler Creative", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5053, "dossier_id": "freepik/magnific/precision", "name": "Magnific Upscaler Precision", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Precision is for logos, UI, text, product photos, scans, and diagrams that must stay faithful.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5054, "dossier_id": "freepik/magnific/precision-v2", "name": "Magnific Upscaler Precision V2", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5055, "dossier_id": "freepik/magnific/relight", "name": "Magnific Relight", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Relight changes the lighting of a scene and can optionally change the background.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5056, "dossier_id": "freepik/magnific/skin-enhancer", "name": "Magnific Skin Enhancer", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Skin Enhancer is for portrait skin, with a faithful path that keeps identity and a creative path that stylizes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5057, "dossier_id": "freepik/magnific/style-transfer", "name": "Magnific Style Transfer", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Style transfer restyles an image from a prompt or a reference while keeping a controllable amount of the original structure.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5058, "dossier_id": "freepik/magnific/upscaler-classic", "name": "Magnific Upscaler Classic", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classic upscaling is the fast preset path when the user does not want Creativity, HDR, and Resemblance sliders.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5059, "dossier_id": "freepik/magnific/video-creative", "name": "Magnific Video Upscaler Creative", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Creative video upscaling invents texture for soft or compressed footage. Turbo is the faster preset of the same upscaler.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5060, "dossier_id": "freepik/magnific/video-precision", "name": "Magnific Video Upscaler Precision", "maker": "freepik", "family": "freepik/magnific", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Precision video upscaling is for footage that must stay true to the source, including faces, logos, and fine patterns.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5061, "dossier_id": "freepik/magnific-one/magnific-one", "name": "Magnific One", "maker": "freepik", "family": "freepik/magnific-one", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5062, "dossier_id": "freepik/moderation/nsfw-image-detector", "name": "NSFW Image Detector", "maker": "freepik", "family": "freepik/moderation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5063, "dossier_id": "freepik/mystic/mystic-1-0", "name": "Mystic 1.0", "maker": "freepik", "family": "freepik/mystic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5064, "dossier_id": "freepik/mystic/mystic-2-5", "name": "Mystic 2.5", "maker": "freepik", "family": "freepik/mystic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5065, "dossier_id": "freepik/mystic/mystic-2-5-flexible", "name": "Mystic 2.5 Flexible", "maker": "freepik", "family": "freepik/mystic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5066, "dossier_id": "freepik/mystic/mystic-2-5-fluid", "name": "Mystic 2.5 Fluid", "maker": "freepik", "family": "freepik/mystic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5067, "dossier_id": "freiburg/demon/demon", "name": "DeMoN", "maker": "freiburg", "family": "freiburg/demon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5068, "dossier_id": "freiburg/dispnet/dispnet", "name": "DispNet", "maker": "freiburg", "family": "freiburg/dispnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5069, "dossier_id": "freiburg/dispnet/dispnet3", "name": "DispNet 3.0", "maker": "freiburg", "family": "freiburg/dispnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5070, "dossier_id": "freiburg/flownet/flownet", "name": "FlowNet", "maker": "freiburg", "family": "freiburg/flownet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5071, "dossier_id": "freiburg/flownet/flownet2", "name": "FlowNet 2.0", "maker": "freiburg", "family": "freiburg/flownet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5072, "dossier_id": "freiburg/flownet/flownet3", "name": "FlowNet 3.0", "maker": "freiburg", "family": "freiburg/flownet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5073, "dossier_id": "freiburg/flownet/flowneth", "name": "FlowNetH", "maker": "freiburg", "family": "freiburg/flownet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5074, "dossier_id": "freiburg/orbis/orbis", "name": "Orbis", "maker": "freiburg", "family": "freiburg/orbis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5075, "dossier_id": "freiburg/sceneflow/sceneflow", "name": "SceneFlow", "maker": "freiburg", "family": "freiburg/sceneflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5076, "dossier_id": "freiburg/unet/3d-u-net", "name": "3D U-Net", "maker": "freiburg", "family": "freiburg/unet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5077, "dossier_id": "freiburg/unet/unet", "name": "U-Net", "maker": "freiburg", "family": "freiburg/unet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5078, "dossier_id": "fudan/anygpt/anygpt", "name": "AnyGPT", "maker": "fudan", "family": "fudan/anygpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says the base model converts among speech, text, images, and music, and the chat model holds multimodal conversations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5079, "dossier_id": "fudan/claif/claif-bert", "name": "CLAIF BERT-base", "maker": "fudan", "family": "fudan/claif", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it maps sentences and paragraphs to a 768-dimensional vector for clustering or semantic search.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5080, "dossier_id": "fudan/claif/claif-roberta", "name": "CLAIF RoBERTa-base", "maker": "fudan", "family": "fudan/claif", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it maps sentences and paragraphs to a 768-dimensional vector for clustering or semantic search.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5081, "dossier_id": "fudan/dirl/dirl-8b-instruct", "name": "DiRL-8B-Instruct", "maker": "fudan", "family": "fudan/dirl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is a mathematical reasoning model and an example of the DiRL training framework.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5082, "dossier_id": "fudan/elasticbert/elasticbert-base", "name": "ElasticBERT-base", "maker": "fudan", "family": "fudan/elasticbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5083, "dossier_id": "fudan/elasticbert/elasticbert-chinese-base", "name": "ElasticBERT-Chinese-base", "maker": "fudan", "family": "fudan/elasticbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same early-exit BERT use as the English base, for Chinese inputs. The card example encodes a short Chinese sentence.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5084, "dossier_id": "fudan/elasticbert/elasticbert-large", "name": "ElasticBERT-large", "maker": "fudan", "family": "fudan/elasticbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5085, "dossier_id": "fudan/from-w1/from-w1", "name": "FRoM-W1", "maker": "fudan", "family": "fudan/from-w1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it turns natural-language instructions into whole-body humanoid motion that a robot can execute.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5086, "dossier_id": "fudan/llama-scope/llama-scope", "name": "Llama Scope", "maker": "fudan", "family": "fudan/llama-scope", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says the SAEs extract interpretable features from every layer and sublayer for mechanistic interpretability.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5087, "dossier_id": "fudan/lorsa/lorsa", "name": "Lorsa", "maker": "fudan", "family": "fudan/lorsa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The README says it decomposes attention-layer outputs into interpretable units to study attention superposition.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5088, "dossier_id": "fudan/moss/moss-base-7b", "name": "MOSS base 7B", "maker": "fudan", "family": "fudan/moss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is a 7 billion parameter pretrained model for later supervised fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5089, "dossier_id": "fudan/moss/moss-rlhf", "name": "MOSS-RLHF 7B English", "maker": "fudan", "family": "fudan/moss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5090, "dossier_id": "fudan/moss-audio/moss-audio-4b-instruct", "name": "MOSS-Audio-4B-Instruct", "maker": "fudan", "family": "fudan/moss-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5091, "dossier_id": "fudan/moss-audio/moss-audio-4b-thinking", "name": "MOSS-Audio-4B-Thinking", "maker": "fudan", "family": "fudan/moss-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5092, "dossier_id": "fudan/moss-audio/moss-audio-8b-instruct", "name": "MOSS-Audio-8B-Instruct", "maker": "fudan", "family": "fudan/moss-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5093, "dossier_id": "fudan/moss-audio/moss-audio-8b-thinking", "name": "MOSS-Audio-8B-Thinking", "maker": "fudan", "family": "fudan/moss-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5094, "dossier_id": "fudan/moss-music/moss-music-8b-instruct", "name": "MOSS-Music-8B-Instruct", "maker": "fudan", "family": "fudan/moss-music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it captions music, transcribes lyrics, and answers questions about structure, harmony, and tempo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5095, "dossier_id": "fudan/moss-music/moss-music-8b-thinking", "name": "MOSS-Music-8B-Thinking", "maker": "fudan", "family": "fudan/moss-music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it captions music, transcribes lyrics, and answers questions about structure, harmony, and tempo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5096, "dossier_id": "fudan/moss-speech/moss-speech", "name": "MOSS-Speech", "maker": "fudan", "family": "fudan/moss-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is for spoken question answering and speech-to-speech dialogue that keeps tone and emotion.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5097, "dossier_id": "fudan/moss-transcribe/moss-transcribe-diarize", "name": "MOSS-Transcribe-Diarize", "maker": "fudan", "family": "fudan/moss-transcribe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5098, "dossier_id": "fudan/moss-transcribe/moss-transcribe-preview-2b", "name": "MOSS-Transcribe-preview-2B", "maker": "fudan", "family": "fudan/moss-transcribe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5099, "dossier_id": "fudan/moss-tts/moss-audio-tokenizer", "name": "MOSS-Audio-Tokenizer", "maker": "fudan", "family": "fudan/moss-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5100, "dossier_id": "fudan/moss-tts/moss-sound-effect", "name": "MOSS-SoundEffect", "maker": "fudan", "family": "fudan/moss-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5101, "dossier_id": "fudan/moss-tts/moss-tts", "name": "MOSS-TTS", "maker": "fudan", "family": "fudan/moss-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5102, "dossier_id": "fudan/moss-tts/moss-tts-local", "name": "MOSS-TTS-Local-Transformer", "maker": "fudan", "family": "fudan/moss-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5103, "dossier_id": "fudan/moss-tts/moss-tts-nano", "name": "MOSS-TTS-Nano", "maker": "fudan", "family": "fudan/moss-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5104, "dossier_id": "fudan/moss-tts/moss-tts-realtime", "name": "MOSS-TTS-Realtime", "maker": "fudan", "family": "fudan/moss-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The family overview says this member is the real-time streaming TTS model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5105, "dossier_id": "fudan/moss-tts/moss-ttsd", "name": "MOSS-TTSD", "maker": "fudan", "family": "fudan/moss-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it generates expressive multi-speaker conversation for podcasts, audiobooks, commentary, and dubbing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5106, "dossier_id": "fudan/moss-tts/moss-voice-generator", "name": "MOSS-VoiceGenerator", "maker": "fudan", "family": "fudan/moss-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The family overview says it designs a voice or character rather than only cloning one.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5107, "dossier_id": "fudan/moss-video/moss-video-preview", "name": "MOSS-Video-Preview", "maker": "fudan", "family": "fudan/moss-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5108, "dossier_id": "fudan/moss-vl/moss-vl", "name": "MOSS-VL", "maker": "fudan", "family": "fudan/moss-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says Instruct-0708 is for image understanding, OCR, documents, visual reasoning, and long-video temporal reasoning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5109, "dossier_id": "fudan/moss-vl/moss-vl-realtime", "name": "MOSS-VL-Realtime", "maker": "fudan", "family": "fudan/moss-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5110, "dossier_id": "fudan/mova/mova", "name": "MOVA", "maker": "fudan", "family": "fudan/mova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5111, "dossier_id": "fudan/omnivae/omnivae", "name": "OmniVAE", "maker": "fudan", "family": "fudan/omnivae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper title and card describe joint audio-video reconstruction and text-to-audio-video generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5112, "dossier_id": "fudan/roboomni/roboomni", "name": "RoboOmni", "maker": "fudan", "family": "fudan/roboomni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it recognizes intent, confirms with speech, and executes manipulation in simulation and the real world.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5113, "dossier_id": "fudan/scijudge/scijudge-30b", "name": "SciJudge-30B", "maker": "fudan", "family": "fudan/scijudge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same pairwise citation-impact judgment as SciJudge-4B, at the 30B-A3B size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5114, "dossier_id": "fudan/scijudge/scijudge-4b", "name": "SciJudge-4B", "maker": "fudan", "family": "fudan/scijudge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it judges scientific papers from title, abstract, and date.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5115, "dossier_id": "fudan/scithinker/scithinker-30b", "name": "SciThinker-30B", "maker": "fudan", "family": "fudan/scithinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it proposes a follow-up paper title and abstract from a seed paper.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5116, "dossier_id": "fudan/scithinker/scithinker-4b", "name": "SciThinker-4B", "maker": "fudan", "family": "fudan/scithinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says that given a seed paper it should propose a new title and abstract with no numeric results.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5117, "dossier_id": "fudan/speechgpt/speechgpt", "name": "SpeechGPT", "maker": "fudan", "family": "fudan/speechgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it perceives and generates speech and text from instructions, including spoken dialogue.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5118, "dossier_id": "fudan/speechgpt/speechgpt-2-0-preview", "name": "SpeechGPT 2.0-preview", "maker": "fudan", "family": "fudan/speechgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is for human-like spoken conversation, style and emotion control, role play, and low-latency interruption.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5119, "dossier_id": "fudan/speechtokenizer/speechtokenizer", "name": "SpeechTokenizer", "maker": "fudan", "family": "fudan/speechtokenizer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it turns speech into semantic and acoustic RVQ codes for speech language models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5120, "dossier_id": "fudan/uslm/uslm", "name": "USLM", "maker": "fudan", "family": "fudan/uslm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5121, "dossier_id": "fujifilm/synapse/synapse-pathology-ai-foundry", "name": "Synapse Pathology AI Foundry", "maker": "fujifilm", "family": "fujifilm/synapse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5122, "dossier_id": "fujitsu/fugaku-llm/fugaku-llm-13b", "name": "Fugaku-LLM 13B", "maker": "fujitsu", "family": "fujitsu/fugaku-llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5123, "dossier_id": "fujitsu/fugaku-llm/fugaku-llm-13b-instruct", "name": "Fugaku-LLM 13B Instruct", "maker": "fujitsu", "family": "fujitsu/fugaku-llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card shows a Japanese instruction format: a system line, then `", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5124, "dossier_id": "fujitsu/fujitsu-llm-kg/fujitsu-llm-kg-8x7b-cpt", "name": "Fujitsu-LLM-KG 8x7B continued pretrain", "maker": "fujitsu", "family": "fujitsu/fujitsu-llm-kg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5125, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5126, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5127, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5128, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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 `", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5129, "dossier_id": "fujitsu/takane/takane", "name": "Takane", "maker": "fujitsu", "family": "fujitsu/takane", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5130, "dossier_id": "galway/an-gaodhal/an-gaodhal-irish-english", "name": "An Gaodhal Irish-English Bilingual Model", "maker": "galway", "family": "galway/an-gaodhal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5131, "dossier_id": "galway/an-gaodhal/an-gaodhal-irish-monolingual", "name": "An Gaodhal Gaeilge / Irish Monolingual Model", "maker": "galway", "family": "galway/an-gaodhal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5132, "dossier_id": "galway/irish-fasttext/early-modern-irish-fasttext", "name": "Early Modern Irish FastText", "maker": "galway", "family": "galway/irish-fasttext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5133, "dossier_id": "galway/irish-fasttext/historical-irish-fasttext", "name": "Historical Irish FastText", "maker": "galway", "family": "galway/irish-fasttext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5134, "dossier_id": "galway/irish-fasttext/middle-irish-fasttext", "name": "Middle Irish FastText", "maker": "galway", "family": "galway/irish-fasttext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5135, "dossier_id": "galway/irish-fasttext/old-irish-fasttext", "name": "Old Irish FastText", "maker": "galway", "family": "galway/irish-fasttext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5136, "dossier_id": "gatech/neural-baby-talk/neural-baby-talk", "name": "Neural Baby Talk", "maker": "gatech", "family": "gatech/neural-baby-talk", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5137, "dossier_id": "gatech/vilbert/vilbert", "name": "ViLBERT", "maker": "gatech", "family": "gatech/vilbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5138, "dossier_id": "geneva/aladdin/llama-aladdin-8b", "name": "Llama-Aladdin-8B", "maker": "geneva", "family": "geneva/aladdin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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\".", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5139, "dossier_id": "geneva/diamond/diamond", "name": "DIAMOND", "maker": "geneva", "family": "geneva/diamond", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5140, "dossier_id": "geneva/iris/delta-iris", "name": "\u0394-IRIS", "maker": "geneva", "family": "geneva/iris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5141, "dossier_id": "geneva/iris/iris", "name": "IRIS", "maker": "geneva", "family": "geneva/iris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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%.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5142, "dossier_id": "genmo/mochi/mochi-1", "name": "Mochi 1", "maker": "genmo", "family": "genmo/mochi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5143, "dossier_id": "genmo/mochi/mochi-1-hd", "name": "Mochi 1 HD", "maker": "genmo", "family": "genmo/mochi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5144, "dossier_id": "genmo/replay/replay", "name": "Replay", "maker": "genmo", "family": "genmo/replay", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5145, "dossier_id": "genoa/co-semdepth/co-semdepth", "name": "Co-SemDepth", "maker": "genoa", "family": "genoa/co-semdepth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5146, "dossier_id": "genoa/hhp-net/hhp-net", "name": "HHP-Net", "maker": "genoa", "family": "genoa/hhp-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5147, "dossier_id": "genoa/m4semantic/m4semantic", "name": "M4Semantic", "maker": "genoa", "family": "genoa/m4semantic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5148, "dossier_id": "ghent/tweety/tweety-7b-dutch", "name": "Tweety-7B Dutch", "maker": "ghent", "family": "ghent/tweety", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5149, "dossier_id": "gist/admet-fm/admet-fm", "name": "ADMET-FM", "maker": "gist", "family": "gist/admet-fm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5150, "dossier_id": "gist/g2d-diff/g2d-diff", "name": "G2D-Diff", "maker": "gist", "family": "gist/g2d-diff", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5151, "dossier_id": "gist/llamp/llamp", "name": "LLAMP", "maker": "gist", "family": "gist/llamp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5152, "dossier_id": "gist/quber/quber", "name": "QuBER", "maker": "gist", "family": "gist/quber", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5153, "dossier_id": "gist/uoais/uoais-net", "name": "UOAIS-Net", "maker": "gist", "family": "gist/uoais", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5154, "dossier_id": "gladia/solaria/solaria-1", "name": "Solaria-1", "maker": "gladia", "family": "gladia/solaria", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5155, "dossier_id": "gladia/solaria/solaria-3", "name": "Solaria-3", "maker": "gladia", "family": "gladia/solaria", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5156, "dossier_id": "gladia/solaria/solaria-fusion", "name": "Solaria Fusion", "maker": "gladia", "family": "gladia/solaria", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5157, "dossier_id": "gladia/whisper-zero/whisper-zero", "name": "Whisper-Zero", "maker": "gladia", "family": "gladia/whisper-zero", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5158, "dossier_id": "glasgow/libra/libra-v1-0-3b", "name": "Libra v1.0 3B", "maker": "glasgow", "family": "glasgow/libra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5159, "dossier_id": "glasgow/plm-interact/plm-interact-35m-human-v11", "name": "PLM-interact-35M-humanV11", "maker": "glasgow", "family": "glasgow/plm-interact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5160, "dossier_id": "glasgow/plm-interact/plm-interact-650m-human-v11", "name": "PLM-interact-650M-humanV11", "maker": "glasgow", "family": "glasgow/plm-interact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5161, "dossier_id": "glasgow/plm-interact/plm-interact-650m-human-v12", "name": "PLM-interact-650M-humanV12", "maker": "glasgow", "family": "glasgow/plm-interact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5162, "dossier_id": "glasgow/plm-interact/plm-interact-650m-leakage-free", "name": "PLM-interact-650M-Leakage-Free", "maker": "glasgow", "family": "glasgow/plm-interact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5163, "dossier_id": "glasgow/plm-interact/plm-interact-650m-mutation", "name": "PLM-interact-650M-Mutation", "maker": "glasgow", "family": "glasgow/plm-interact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5164, "dossier_id": "glasgow/plm-interact/plm-interact-650m-vh", "name": "PLM-interact-650M-VH", "maker": "glasgow", "family": "glasgow/plm-interact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5165, "dossier_id": "goettingen/privi/privi", "name": "PriVi", "maker": "goettingen", "family": "goettingen/privi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5166, "dossier_id": "goettingen/treelearn/treelearn", "name": "TreeLearn", "maker": "goettingen", "family": "goettingen/treelearn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5167, "dossier_id": "google/chirp/chirp", "name": "Chirp", "maker": "google", "family": "google/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.016, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual automatic speech recognition.", "summary_intro": "Google Chirp speech-to-text model, first generation of the Chirp ASR line. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5168, "dossier_id": "google/chirp/chirp-2", "name": "Chirp 2", "maker": "google", "family": "google/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.016, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Transcribe speech, with timestamps, adaptation, and speech translation.", "summary_intro": "Google Chirp 2 speech recognition model on Speech-to-Text v2. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5169, "dossier_id": "google/chirp/chirp-3", "name": "Chirp 3", "maker": "google", "family": "google/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.016, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual transcription with diarization and language detection.", "summary_intro": "Google Chirp 3 speech-to-text model. Docs were current as of October 13, 2026. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5170, "dossier_id": "google/chirp/chirp-3-hd", "name": "Chirp 3 HD", "maker": "google", "family": "google/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate spoken audio from text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5171, "dossier_id": "google/codegemma/codegemma-2b", "name": "CodeGemma 2B", "maker": "google", "family": "google/codegemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation and code completion.", "summary_intro": "Google open-weights code model from the Gemma 1 generation. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5172, "dossier_id": "google/codegemma/codegemma-7b", "name": "CodeGemma 7B", "maker": "google", "family": "google/codegemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation and code completion.", "summary_intro": "Google open-weights code model from the Gemma 1 generation. Collected 2026-10-15. Weights remain published.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5173, "dossier_id": "google/datagemma/datagemma", "name": "DataGemma", "maker": "google", "family": "google/datagemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The releases index names the model. The task description on the DataGemma doc page was not opened.", "summary_intro": "Google DataGemma research model released at the 2B size. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5174, "dossier_id": "google/embedding/multimodalembedding", "name": "Multimodal Embedding", "maker": "google", "family": "google/embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and multimodal vectors for search and classification.", "summary_intro": "Google multimodal embedding model listed on Vertex AI, separate from Gemini Embedding. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5175, "dossier_id": "google/functiongemma/functiongemma", "name": "FunctionGemma", "maker": "google", "family": "google/functiongemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Function calling after further fine-tuning. Agent Platform says it is built to be tuned for that job.", "summary_intro": "Google 270M open model intended as a base for specialized function-calling models. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5176, "dossier_id": "google/gecko/textembedding-gecko", "name": "Text Embedding Gecko", "maker": "google", "family": "google/gecko", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings for search, classification, and clustering.", "summary_intro": "Retired Google text embedding model from the Gecko line on Vertex AI. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5177, "dossier_id": "google/gecko/textembedding-gecko-multilingual", "name": "Text Embedding Gecko Multilingual", "maker": "google", "family": "google/gecko", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual text embeddings.", "summary_intro": "Retired multilingual Gecko text embedding model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5178, "dossier_id": "google/gemini/gemini-1-0-pro-vision", "name": "Gemini 1.0 Pro Vision", "maker": "google", "family": "google/gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5179, "dossier_id": "google/gemini/gemini-1-5-flash-8b", "name": "Gemini 1.5 Flash-8B", "maker": "google", "family": "google/gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5180, "dossier_id": "google/gemini/gemini-2-0-flash-thinking", "name": "Gemini 2.0 Flash Thinking", "maker": "google", "family": "google/gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Gemini 2.0 Flash Thinking was a family of experimental reasoning endpoints. Google shut them down on 2025-12-02.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5181, "dossier_id": "google/gemini/gemini-2-0-pro", "name": "Gemini 2.0 Pro", "maker": "google", "family": "google/gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5182, "dossier_id": "google/gemini/gemini-2-5-deep-think", "name": "Gemini 2.5 Deep Think", "maker": "google", "family": "google/gemini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5183, "dossier_id": "google/gemini-deep-research/gemini-deep-research-pro", "name": "Gemini Deep Research Pro", "maker": "google", "family": "google/gemini-deep-research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The deprecations page lists it as the earlier managed research agent, replaced by deep-research-preview-04-2026.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5184, "dossier_id": "google/gemini-image/gemini-2-0-flash-image", "name": "Gemini 2.0 Flash Image", "maker": "google", "family": "google/gemini-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It was the preview native image endpoint on Gemini 2.0 Flash before Gemini 2.5 Flash Image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5185, "dossier_id": "google/gemini-live/gemini-2-0-flash-live", "name": "Gemini 2.0 Flash Live", "maker": "google", "family": "google/gemini-live", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The changelog calls it a public preview of the Live API on Gemini 2.0 Flash.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5186, "dossier_id": "google/gemini-nano/gemini-nano", "name": "Gemini Nano", "maker": "google", "family": "google/gemini-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5187, "dossier_id": "google/gemini-robotics/gemini-robotics", "name": "Gemini Robotics", "maker": "google", "family": "google/gemini-robotics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5188, "dossier_id": "google/gemini-robotics/gemini-robotics-2", "name": "Gemini Robotics 2", "maker": "google", "family": "google/gemini-robotics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepMind describes it as the intelligence layer that turns vision and language into motor control for many robot bodies.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5189, "dossier_id": "google/gemini-robotics/gemini-robotics-er-1-5", "name": "Gemini Robotics-ER 1.5", "maker": "google", "family": "google/gemini-robotics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5190, "dossier_id": "google/gemini-robotics/gemini-robotics-on-device-2", "name": "Gemini Robotics On-Device 2", "maker": "google", "family": "google/gemini-robotics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DeepMind describes a local VLA that adapts to new robots, objects, and scenes without a network.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5191, "dossier_id": "google/gemini-transcribe/gemini-3-5-transcribe", "name": "Gemini 3.5 Transcribe", "maker": "google", "family": "google/gemini-transcribe", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 12.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Google built it for low-latency transcription with utterance language detection, speaker diarization, word timestamps, smart formatting, and vocabulary biasing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5192, "dossier_id": "google/gemma/gemma-2-2b-jpn", "name": "Gemma 2 JPN 2B", "maker": "google", "family": "google/gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese text tasks. The releases index is the source used for identity and date.", "summary_intro": "Google open-weights Japanese Gemma 2 2B model, named on the official Gemma releases index. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5193, "dossier_id": "google/gemma/gemma-2b", "name": "Gemma 2B", "maker": "google", "family": "google/gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation, summarization, and extraction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5194, "dossier_id": "google/gemma/gemma-7b", "name": "Gemma 7B", "maker": "google", "family": "google/gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text generation, summarization, and extraction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5195, "dossier_id": "google/gemma-aps/gemma-aps-2b", "name": "Gemma-APS 2B", "maker": "google", "family": "google/gemma-aps", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The releases index names the sizes. The method page was not opened.", "summary_intro": "Google Gemma-APS model named on the Gemma releases index. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5196, "dossier_id": "google/gemma-aps/gemma-aps-7b", "name": "Gemma-APS 7B", "maker": "google", "family": "google/gemma-aps", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The releases index names the sizes. The method page was not opened.", "summary_intro": "Google Gemma-APS model named on the Gemma releases index. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5197, "dossier_id": "google/genie/genie-3", "name": "Genie 3", "maker": "google", "family": "google/genie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Simulate environments and generate interactive worlds.", "summary_intro": "Google DeepMind world model Genie 3, listed on the official models index. Collected 2026-10-15. Detailed specs were not opened.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5198, "dossier_id": "google/imagen/imagen-2", "name": "Imagen 2", "maker": "google", "family": "google/imagen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate and edit images from text, including mask edits.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5199, "dossier_id": "google/imagen/imagen-3", "name": "Imagen 3", "maker": "google", "family": "google/imagen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation, plus editing and customization on the Vertex Imagen 3 editing model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5200, "dossier_id": "google/imagen/imagen-4", "name": "Imagen 4", "maker": "google", "family": "google/imagen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-resolution text-to-image generation, with a Fast tier for latency and an Ultra tier for prompt adherence.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5201, "dossier_id": "google/imagen/imagen-4-fast", "name": "Imagen 4 Fast", "maker": "google", "family": "google/imagen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.02, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-resolution text-to-image generation, with a Fast tier for latency and an Ultra tier for prompt adherence.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5202, "dossier_id": "google/imagen/imagen-4-ultra", "name": "Imagen 4 Ultra", "maker": "google", "family": "google/imagen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.06, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-resolution text-to-image generation, with a Fast tier for latency and an Ultra tier for prompt adherence.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5203, "dossier_id": "google/lyria/lyria-2", "name": "Lyria 2", "maker": "google", "family": "google/lyria", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.06, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate instrumental music from an English text prompt.", "summary_intro": "Google Lyria 2 music model on Vertex AI, model id lyria-002. Collected 2026-10-15. Instrumental clips from text.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5204, "dossier_id": "google/lyria/lyria-3-5", "name": "Lyria 3.5", "maker": "google", "family": "google/lyria", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Music generation with stronger vocals, lyrics, and variable song length.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5205, "dossier_id": "google/lyria/lyria-realtime", "name": "Lyria RealTime", "maker": "google", "family": "google/lyria", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Interactive, streaming music generation.", "summary_intro": "Google real-time music model listed in the Lyria family. Collected 2026-10-15. Detailed specs were not opened.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5206, "dossier_id": "google/magenta/magenta-realtime", "name": "Magenta RealTime", "maker": "google", "family": "google/magenta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time music generation as an open creative tool.", "summary_intro": "Google Magenta real-time open music model, listed by DeepMind as the open Lyria model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5207, "dossier_id": "google/medgemma/medgemma-27b", "name": "MedGemma 27B", "maker": "google", "family": "google/medgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical text and image comprehension on a larger checkpoint.", "summary_intro": "Google 27B health model based on Gemma 3. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5208, "dossier_id": "google/medgemma/medgemma-4b", "name": "MedGemma 4B", "maker": "google", "family": "google/medgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical text and image comprehension.", "summary_intro": "Google health model based on Gemma 3 for medical text and image understanding. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5209, "dossier_id": "google/medsiglip/medsiglip", "name": "MedSigLIP", "maker": "google", "family": "google/medsiglip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Encode medical images and text into a shared embedding space.", "summary_intro": "Google medical embedding model, a SigLIP variant, listed with the Gemma open models on Agent Platform. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5210, "dossier_id": "google/paligemma/paligemma-2-10b", "name": "PaliGemma 2 10B", "maker": "google", "family": "google/paligemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and short-video questions, captioning, and transfer fine-tuning.", "summary_intro": "Google open vision-language model combining SigLIP-So400m with Gemma 2. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5211, "dossier_id": "google/paligemma/paligemma-2-28b", "name": "PaliGemma 2 28B", "maker": "google", "family": "google/paligemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and short-video questions, captioning, and transfer fine-tuning.", "summary_intro": "Google open vision-language model combining SigLIP-So400m with Gemma 2. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5212, "dossier_id": "google/paligemma/paligemma-2-3b", "name": "PaliGemma 2 3B", "maker": "google", "family": "google/paligemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and short-video questions, captioning, and transfer fine-tuning.", "summary_intro": "Google open vision-language model combining SigLIP-So400m with Gemma 2. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5213, "dossier_id": "google/paligemma/paligemma-3b", "name": "PaliGemma 3B", "maker": "google", "family": "google/paligemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image captioning and visual question answering.", "summary_intro": "Google open vision-language model that pairs a vision encoder with Gemma. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5214, "dossier_id": "google/palm/chat-bison", "name": "chat-bison", "maker": "google", "family": "google/palm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chat", "summary_intro": "Retired Google PaLM 2 API model chat-bison. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5215, "dossier_id": "google/palm/code-bison", "name": "code-bison", "maker": "google", "family": "google/palm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation", "summary_intro": "Retired Google PaLM 2 API model code-bison. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5216, "dossier_id": "google/palm/code-gecko", "name": "code-gecko", "maker": "google", "family": "google/palm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code completion", "summary_intro": "Retired Google PaLM 2 API model code-gecko. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5217, "dossier_id": "google/palm/codechat-bison", "name": "codechat-bison", "maker": "google", "family": "google/palm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code chat", "summary_intro": "Retired Google PaLM 2 API model codechat-bison. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5218, "dossier_id": "google/palm/palm", "name": "PaLM", "maker": "google", "family": "google/palm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General language modeling research release.", "summary_intro": "Google Research Pathways language model announced before PaLM 2. Collected 2026-10-15. Not a current API.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5219, "dossier_id": "google/palm/palm-2", "name": "PaLM 2", "maker": "google", "family": "google/palm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text, chat, and code generation. Product endpoints are cataloged separately.", "summary_intro": "Google PaLM 2 language model family behind the retired Vertex and PaLM APIs. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5220, "dossier_id": "google/palm/text-unicorn", "name": "text-unicorn", "maker": "google", "family": "google/palm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger text generation", "summary_intro": "Retired Google PaLM 2 API model text-unicorn. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5221, "dossier_id": "google/recurrentgemma/recurrentgemma-9b", "name": "RecurrentGemma 9B", "maker": "google", "family": "google/recurrentgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Google open RecurrentGemma 9B model, named on the Gemma releases index. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5222, "dossier_id": "google/shieldgemma/shieldgemma-2", "name": "ShieldGemma 2", "maker": "google", "family": "google/shieldgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Check synthetic and natural images, and text, against safety policies.", "summary_intro": "Google 4B image-and-text safety classifier built on Gemma 3. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5223, "dossier_id": "google/shieldgemma/shieldgemma-27b", "name": "ShieldGemma 27B", "maker": "google", "family": "google/shieldgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score text inputs and outputs against defined safety policies.", "summary_intro": "Google open safety classifier built on Gemma 2. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5224, "dossier_id": "google/shieldgemma/shieldgemma-2b", "name": "ShieldGemma 2B", "maker": "google", "family": "google/shieldgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score text inputs and outputs against defined safety policies.", "summary_intro": "Google open safety classifier built on Gemma 2. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5225, "dossier_id": "google/shieldgemma/shieldgemma-9b", "name": "ShieldGemma 9B", "maker": "google", "family": "google/shieldgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score text inputs and outputs against defined safety policies.", "summary_intro": "Google open safety classifier built on Gemma 2. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5226, "dossier_id": "google/t5gemma/t5gemma-2-1b-1b", "name": "T5Gemma 2 1B-1B", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generative tasks. Agent Platform says T5Gemma 2 suits question answering, summarization, and reasoning.", "summary_intro": "Google T5Gemma 2 encoder-decoder size named on the Gemma releases index. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5227, "dossier_id": "google/t5gemma/t5gemma-2-270m-270m", "name": "T5Gemma 2 270M-270M", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generative tasks. Agent Platform says T5Gemma 2 suits question answering, summarization, and reasoning.", "summary_intro": "Google T5Gemma 2 encoder-decoder size named on the Gemma releases index. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5228, "dossier_id": "google/t5gemma/t5gemma-2-4b-4b", "name": "T5Gemma 2 4B-4B", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generative tasks. Agent Platform says T5Gemma 2 suits question answering, summarization, and reasoning.", "summary_intro": "Google T5Gemma 2 encoder-decoder size named on the Gemma releases index. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5229, "dossier_id": "google/t5gemma/t5gemma-2b-2b", "name": "T5Gemma 2B-2B", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5230, "dossier_id": "google/t5gemma/t5gemma-9b-2b", "name": "T5Gemma 9B-2B", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5231, "dossier_id": "google/t5gemma/t5gemma-9b-9b", "name": "T5Gemma 9B-9B", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5232, "dossier_id": "google/t5gemma/t5gemma-b-b", "name": "T5Gemma B-B", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5233, "dossier_id": "google/t5gemma/t5gemma-l-l", "name": "T5Gemma L-L", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5234, "dossier_id": "google/t5gemma/t5gemma-m-l", "name": "T5Gemma M-L", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5235, "dossier_id": "google/t5gemma/t5gemma-s-s", "name": "T5Gemma S-S", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5236, "dossier_id": "google/t5gemma/t5gemma-xl-xl", "name": "T5Gemma XL-XL", "maker": "google", "family": "google/t5gemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Question answering, summarization, and reasoning, per the Agent Platform model list.", "summary_intro": "Google encoder-decoder T5Gemma model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5237, "dossier_id": "google/translategemma/translategemma-12b", "name": "TranslateGemma 12B", "maker": "google", "family": "google/translategemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Translation. Agent Platform says the family handles 55 languages.", "summary_intro": "Google open translation model in the TranslateGemma family. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5238, "dossier_id": "google/translategemma/translategemma-27b", "name": "TranslateGemma 27B", "maker": "google", "family": "google/translategemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Translation. Agent Platform says the family handles 55 languages.", "summary_intro": "Google open translation model in the TranslateGemma family. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5239, "dossier_id": "google/translategemma/translategemma-4b", "name": "TranslateGemma 4B", "maker": "google", "family": "google/translategemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Translation. Agent Platform says the family handles 55 languages.", "summary_intro": "Google open translation model in the TranslateGemma family. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5240, "dossier_id": "google/txgemma/txgemma-27b", "name": "TxGemma 27B", "maker": "google", "family": "google/txgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Therapeutic classification, regression, generation, and reasoning, per Agent Platform.", "summary_intro": "Google therapeutic open model in the TxGemma family. Collected 2026-10-15. Release date was not on the pages used.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5241, "dossier_id": "google/txgemma/txgemma-2b", "name": "TxGemma 2B", "maker": "google", "family": "google/txgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Therapeutic classification, regression, generation, and reasoning, per Agent Platform.", "summary_intro": "Google therapeutic open model in the TxGemma family. Collected 2026-10-15. Release date was not on the pages used.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5242, "dossier_id": "google/txgemma/txgemma-9b", "name": "TxGemma 9B", "maker": "google", "family": "google/txgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Therapeutic classification, regression, generation, and reasoning, per Agent Platform.", "summary_intro": "Google therapeutic open model in the TxGemma family. Collected 2026-10-15. Release date was not on the pages used.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5243, "dossier_id": "google/vaultgemma/vaultgemma", "name": "VaultGemma", "maker": "google", "family": "google/vaultgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The releases index names the model and size. The purpose page was not opened.", "summary_intro": "Google VaultGemma 1B open model named on the Gemma releases index. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5244, "dossier_id": "google/veo/veo-2", "name": "Veo 2", "maker": "google", "family": "google/veo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "summary_intro": "Earlier Veo generation listed on Vertex model pages before Veo 3.1. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5245, "dossier_id": "google/veo/veo-3", "name": "Veo 3", "maker": "google", "family": "google/veo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "summary_intro": "Google video model with native audio, announced May 20, 2025. Later tiers are Veo 3.1. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5246, "dossier_id": "google/veo/veo-3-1", "name": "Veo 3.1", "maker": "google", "family": "google/veo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.4, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "summary_intro": "Google's current leading Veo video model with native audio. Agent Platform documents veo-3.1-generate-001. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5247, "dossier_id": "google/veo/veo-3-1-fast", "name": "Veo 3.1 Fast", "maker": "google", "family": "google/veo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.1, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "summary_intro": "Lower-latency Veo 3.1 tier. Agent Platform id veo-3.1-fast-generate-001. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5248, "dossier_id": "google/veo/veo-3-1-lite", "name": "Veo 3.1 Lite", "maker": "google", "family": "google/veo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5249, "dossier_id": "google/veo/veo-3-fast", "name": "Veo 3 Fast", "maker": "google", "family": "google/veo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate video from text and images. Veo 3 and later can generate audio with the video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5250, "dossier_id": "gothenburg/flair/flair-pos-swedish", "name": "Flair Swedish POS tagger", "maker": "gothenburg", "family": "gothenburg/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5251, "dossier_id": "gothenburg/kubhist/kubhist-diachronic-embeddings", "name": "Kubhist diachronic embeddings", "maker": "gothenburg", "family": "gothenburg/kubhist", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5252, "dossier_id": "gothenburg/kubord/kubord-fasttext-aftonbladet-lemma", "name": "Kubord fastText Aftonbladet lemma", "maker": "gothenburg", "family": "gothenburg/kubord", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5253, "dossier_id": "gothenburg/kubord/kubord-fasttext-aftonbladet-token", "name": "Kubord fastText Aftonbladet token", "maker": "gothenburg", "family": "gothenburg/kubord", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5254, "dossier_id": "gothenburg/kubord/kubord-fasttext-dagens-nyheter-lemma", "name": "Kubord fastText Dagens Nyheter lemma", "maker": "gothenburg", "family": "gothenburg/kubord", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5255, "dossier_id": "gothenburg/kubord/kubord-fasttext-dagens-nyheter-token", "name": "Kubord fastText Dagens Nyheter token", "maker": "gothenburg", "family": "gothenburg/kubord", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5256, "dossier_id": "gothenburg/kubord/kubord-fasttext-goteborgsposten-lemma", "name": "Kubord fastText G\u00f6teborgsposten lemma", "maker": "gothenburg", "family": "gothenburg/kubord", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5257, "dossier_id": "gothenburg/kubord/kubord-fasttext-goteborgsposten-token", "name": "Kubord fastText G\u00f6teborgsposten token", "maker": "gothenburg", "family": "gothenburg/kubord", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5258, "dossier_id": "gothenburg/marmot/marmot-pos-swedish", "name": "Marmot Swedish POS tagger", "maker": "gothenburg", "family": "gothenburg/marmot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5259, "dossier_id": "gothenburg/stanza/stanza-depparse-swedish", "name": "Stanza Swedish dependency parser", "maker": "gothenburg", "family": "gothenburg/stanza", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5260, "dossier_id": "gothenburg/stanza/stanza-lemma-swedish", "name": "Stanza Swedish lemmatizer", "maker": "gothenburg", "family": "gothenburg/stanza", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5261, "dossier_id": "gothenburg/stanza/stanza-pos-swedish", "name": "Stanza Swedish POS tagger", "maker": "gothenburg", "family": "gothenburg/stanza", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5262, "dossier_id": "gothenburg/superlim/superlim-absabank-imm", "name": "SuperLim Absabank-Imm BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence classification of attitude toward immigration. The Sparv plugin records a float from 1 to 5.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5263, "dossier_id": "gothenburg/superlim/superlim-argumentation", "name": "SuperLim argumentation-sentence BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence stance: pro, con, or neutral, as defined by the Sparv-Superlim plugin.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5264, "dossier_id": "gothenburg/superlim/superlim-dalaj-ged", "name": "SuperLim DaLAJ-GED BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence classification of Swedish as correct or incorrect (DaLAJ-GED), as defined by the Sparv-Superlim plugin.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5265, "dossier_id": "gothenburg/superlim/superlim-swenli", "name": "SuperLim SweNLI BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sentence-pair labels entailment, contradiction, and neutral, as defined by the Sparv-Superlim plugin.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5266, "dossier_id": "gothenburg/superlim/superlim-sweparaphrase", "name": "SuperLim SweParaphrase BERT", "maker": "gothenburg", "family": "gothenburg/superlim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5267, "dossier_id": "gothenburg/wiki2vec/wiki-word2vec-en", "name": "English Wikipedia word2vec", "maker": "gothenburg", "family": "gothenburg/wiki2vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5268, "dossier_id": "groningen/wav2vec2/wav2vec2-dutch-base", "name": "Wav2Vec2 Dutch base", "maker": "groningen", "family": "groningen/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5269, "dossier_id": "groningen/wav2vec2/wav2vec2-dutch-large", "name": "Wav2Vec2 Dutch large", "maker": "groningen", "family": "groningen/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5270, "dossier_id": "guelph/barcodemamba/barcodemamba", "name": "BarcodeMamba", "maker": "guelph", "family": "guelph/barcodemamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5271, "dossier_id": "haifa/vad/vad-binomial", "name": "VAD Binomial Regression Models", "maker": "haifa", "family": "haifa/vad", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5272, "dossier_id": "haiper/video/haiper-1", "name": "Haiper 1.0", "maker": "haiper", "family": "haiper/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5273, "dossier_id": "haiper/video/haiper-1-5", "name": "Haiper Video 1.5", "maker": "haiper", "family": "haiper/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5274, "dossier_id": "haiper/video/haiper-video-2-x", "name": "Haiper Video 2.x", "maker": "haiper", "family": "haiper/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5275, "dossier_id": "hamburg/amharic/am-flair-ner", "name": "Amharic FLAIR NER", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5276, "dossier_id": "hamburg/amharic/am-flair-pos", "name": "Amharic FLAIR POS", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5277, "dossier_id": "hamburg/amharic/am-flair-sent", "name": "Amharic FLAIR sentiment", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5278, "dossier_id": "hamburg/amharic/am-forward", "name": "Amharic FLAIR forward", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5279, "dossier_id": "hamburg/amharic/am-roberta", "name": "Amharic RoBERTa", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5280, "dossier_id": "hamburg/amharic/amharic-fasttext", "name": "Amharic fastText 300D", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5281, "dossier_id": "hamburg/amharic/amharic-hate-speech", "name": "Amharic hate speech", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5282, "dossier_id": "hamburg/amharic/amharic-question-classification", "name": "Amharic question classification", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5283, "dossier_id": "hamburg/amharic/amharic-word2vec", "name": "Amharic word2vec 300D", "maker": "hamburg", "family": "hamburg/amharic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5284, "dossier_id": "hamburg/sgmse/sgmse-plus", "name": "SGMSE+", "maker": "hamburg", "family": "hamburg/sgmse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5285, "dossier_id": "hamburg/storm/storm", "name": "StoRM", "maker": "hamburg", "family": "hamburg/storm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5286, "dossier_id": "hamburg/streamfm/stream-fm", "name": "Stream.FM", "maker": "hamburg", "family": "hamburg/streamfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5287, "dossier_id": "hamburg/tuda-de/tuda-de", "name": "TUDA-DE German ASR", "maker": "hamburg", "family": "hamburg/tuda-de", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5288, "dossier_id": "hanyang/s2rl/s2rl", "name": "S2RL", "maker": "hanyang", "family": "hanyang/s2rl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5289, "dossier_id": "harvard/adapt/adapt", "name": "ADAPT", "maker": "harvard", "family": "harvard/adapt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5290, "dossier_id": "harvard/athena/athena-r1", "name": "ATHENA-R1", "maker": "harvard", "family": "harvard/athena", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5291, "dossier_id": "harvard/atomica/atomica", "name": "ATOMICA", "maker": "harvard", "family": "harvard/atomica", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5292, "dossier_id": "harvard/axonet/axonet", "name": "AxoNet", "maker": "harvard", "family": "harvard/axonet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5293, "dossier_id": "harvard/boomerang/boomerang", "name": "Boomerang", "maker": "harvard", "family": "harvard/boomerang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5294, "dossier_id": "harvard/carta/carta", "name": "CARTA", "maker": "harvard", "family": "harvard/carta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5295, "dossier_id": "harvard/cascade/cascade", "name": "CASCADE", "maker": "harvard", "family": "harvard/cascade", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5296, "dossier_id": "harvard/chief/chief", "name": "CHIEF", "maker": "harvard", "family": "harvard/chief", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5297, "dossier_id": "harvard/clinical-bert/bio-clinicalbert", "name": "Bio+Clinical BERT", "maker": "harvard", "family": "harvard/clinical-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5298, "dossier_id": "harvard/clinical-bert/bio-discharge-summary-bert", "name": "Bio+Discharge Summary BERT", "maker": "harvard", "family": "harvard/clinical-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5299, "dossier_id": "harvard/conch/conch", "name": "CONCH", "maker": "harvard", "family": "harvard/conch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5300, "dossier_id": "harvard/conch/conch-1-5", "name": "CONCH v1.5", "maker": "harvard", "family": "harvard/conch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "TITAN uses it as the patch encoder for whole-slide features. The public v1.5 file is vision-only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5301, "dossier_id": "harvard/eve/eve", "name": "EVE", "maker": "harvard", "family": "harvard/eve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5302, "dossier_id": "harvard/eve/evescape", "name": "EVEscape", "maker": "harvard", "family": "harvard/eve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5303, "dossier_id": "harvard/eve/popeve", "name": "popEVE", "maker": "harvard", "family": "harvard/eve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5304, "dossier_id": "harvard/eve/tranception", "name": "Tranception", "maker": "harvard", "family": "harvard/eve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5305, "dossier_id": "harvard/hest/hest-tissue-seg", "name": "HEST tissue segmenter", "maker": "harvard", "family": "harvard/hest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5306, "dossier_id": "harvard/hupd/hupd-distilroberta", "name": "HUPD DistilRoBERTa", "maker": "harvard", "family": "harvard/hupd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It continues DistilRoBERTa on English patent text. The card shows a fill-mask example and points at patentdataset.org.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5307, "dossier_id": "harvard/hupd/hupd-t5-small", "name": "HUPD T5-small", "maker": "harvard", "family": "harvard/hupd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5308, "dossier_id": "harvard/kronos/kronos", "name": "KRONOS", "maker": "harvard", "family": "harvard/kronos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It was released to embed multiplex immunofluorescence patches without assuming one fixed antibody panel.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5309, "dossier_id": "harvard/kronos/kronos-2", "name": "KRONOS2", "maker": "harvard", "family": "harvard/kronos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It embeds a stack of single-marker patches conditioned on marker names, so one model can take different antibody panels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5310, "dossier_id": "harvard/madeleine/madeleine", "name": "MADELEINE", "maker": "harvard", "family": "harvard/madeleine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5311, "dossier_id": "harvard/madrigal/madrigal", "name": "Madrigal", "maker": "harvard", "family": "harvard/madrigal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5312, "dossier_id": "harvard/medtok/medtok", "name": "MedTok", "maker": "harvard", "family": "harvard/medtok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5313, "dossier_id": "harvard/pathchat/pathchat", "name": "PathChat", "maker": "harvard", "family": "harvard/pathchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5314, "dossier_id": "harvard/procyon/procyon", "name": "ProCyon", "maker": "harvard", "family": "harvard/procyon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5315, "dossier_id": "harvard/proton/proton", "name": "PROTON", "maker": "harvard", "family": "harvard/proton", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5316, "dossier_id": "harvard/seal/seal", "name": "SEAL", "maker": "harvard", "family": "harvard/seal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5317, "dossier_id": "harvard/spatia/spatia", "name": "SPATIA", "maker": "harvard", "family": "harvard/spatia", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5318, "dossier_id": "harvard/titan/titan", "name": "TITAN", "maker": "harvard", "family": "harvard/titan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5319, "dossier_id": "harvard/txagent/toolrag", "name": "ToolRAG-T1", "maker": "harvard", "family": "harvard/txagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5320, "dossier_id": "harvard/txagent/txagent", "name": "TxAgent-T1", "maker": "harvard", "family": "harvard/txagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5321, "dossier_id": "harvard/txgnn/txgnn", "name": "TxGNN", "maker": "harvard", "family": "harvard/txgnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It predicts indications and contraindications for diseases that had no treatments in training. A public explorer is at txgnn.org.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5322, "dossier_id": "harvard/uni/uni", "name": "UNI", "maker": "harvard", "family": "harvard/uni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5323, "dossier_id": "harvard/uni/uni-2", "name": "UNI 2", "maker": "harvard", "family": "harvard/uni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5324, "dossier_id": "hawaii/wv-net/wv-net", "name": "WV-Net", "maker": "hawaii", "family": "hawaii/wv-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5325, "dossier_id": "hedra/avatar/hedra-avatar", "name": "Hedra Avatar", "maker": "hedra", "family": "hedra/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5326, "dossier_id": "hedra/character/character-1", "name": "Character-1", "maker": "hedra", "family": "hedra/character", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5327, "dossier_id": "hedra/character/character-2", "name": "Character-2", "maker": "hedra", "family": "hedra/character", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5328, "dossier_id": "hedra/character/character-3", "name": "Hedra Character 3", "maker": "hedra", "family": "hedra/character", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5329, "dossier_id": "hedra/omnia/omnia", "name": "Hedra Omnia", "maker": "hedra", "family": "hedra/omnia", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5330, "dossier_id": "heidelberg/classical/grberta", "name": "Gr\u03b5BERTa", "maker": "heidelberg", "family": "heidelberg/classical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5331, "dossier_id": "heidelberg/classical/greta", "name": "Gr\u03b5Ta", "maker": "heidelberg", "family": "heidelberg/classical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5332, "dossier_id": "heidelberg/classical/laberta", "name": "LaBERTa", "maker": "heidelberg", "family": "heidelberg/classical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5333, "dossier_id": "heidelberg/classical/lata", "name": "LaTa", "maker": "heidelberg", "family": "heidelberg/classical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5334, "dossier_id": "heidelberg/classical/philberta", "name": "PhilBERTa", "maker": "heidelberg", "family": "heidelberg/classical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5335, "dossier_id": "heidelberg/classical/philta", "name": "PhilTa", "maker": "heidelberg", "family": "heidelberg/classical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5336, "dossier_id": "heidelberg/classical/sphilberta", "name": "SPhilBERTa", "maker": "heidelberg", "family": "heidelberg/classical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5337, "dossier_id": "heidelberg/imagebart/imagebart", "name": "ImageBART", "maker": "heidelberg", "family": "heidelberg/imagebart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5338, "dossier_id": "heidelberg/scene-synthesis/scene-synthesis", "name": "Scene Synthesis", "maker": "heidelberg", "family": "heidelberg/scene-synthesis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5339, "dossier_id": "heidelberg/taming/taming-transformer", "name": "Taming Transformer", "maker": "heidelberg", "family": "heidelberg/taming", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5340, "dossier_id": "heidelberg/vqgan/vqgan", "name": "VQGAN", "maker": "heidelberg", "family": "heidelberg/vqgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5341, "dossier_id": "helsinki/emma/emma-500-llama2-7b", "name": "EMMA-500 Llama 2 7B", "maker": "helsinki", "family": "helsinki/emma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5342, "dossier_id": "helsinki/emma/emma-500-llama3-1-8b-bi", "name": "EMMA-500 Llama 3.1 8B Bi", "maker": "helsinki", "family": "helsinki/emma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5343, "dossier_id": "helsinki/emma/emma-500-llama3-1-8b-mono", "name": "EMMA-500 Llama 3.1 8B Mono", "maker": "helsinki", "family": "helsinki/emma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5344, "dossier_id": "helsinki/emma/emma-500-llama3-8b-bi", "name": "EMMA-500 Llama 3 8B Bi", "maker": "helsinki", "family": "helsinki/emma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5345, "dossier_id": "helsinki/emma/emma-500-llama3-8b-mono", "name": "EMMA-500 Llama 3 8B Mono", "maker": "helsinki", "family": "helsinki/emma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5346, "dossier_id": "helsinki/mala/mala-500", "name": "MaLA-500", "maker": "helsinki", "family": "helsinki/mala", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5347, "dossier_id": "helsinki/opus-mt/opus-mt", "name": "OPUS-MT", "maker": "helsinki", "family": "helsinki/opus-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5348, "dossier_id": "helsinki/opus-mt/opus-mt-esperanto", "name": "OPUS-MT Esperanto", "maker": "helsinki", "family": "helsinki/opus-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5349, "dossier_id": "helsinki/opus-mt/opus-mt-tc-base", "name": "OPUS-MT Tatoeba base", "maker": "helsinki", "family": "helsinki/opus-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5350, "dossier_id": "helsinki/opus-mt/opus-mt-tc-bible", "name": "OPUS-MT Tatoeba Bible big", "maker": "helsinki", "family": "helsinki/opus-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5351, "dossier_id": "helsinki/opus-mt/opus-mt-tc-big", "name": "OPUS-MT Tatoeba big", "maker": "helsinki", "family": "helsinki/opus-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5352, "dossier_id": "helsinki/opus-mt/opus-mt-tiny", "name": "OPUS-MT tiny", "maker": "helsinki", "family": "helsinki/opus-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5353, "dossier_id": "helsinki/opus-mt/opus-tatoeba", "name": "OPUS-MT Tatoeba ports", "maker": "helsinki", "family": "helsinki/opus-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5354, "dossier_id": "helsinki/simplify/fin-simple-mbart", "name": "Finnish simple mBART", "maker": "helsinki", "family": "helsinki/simplify", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5355, "dossier_id": "helsinki/simplify/simple-finnish-gpt3-xl", "name": "Simple Finnish GPT-3 XL", "maker": "helsinki", "family": "helsinki/simplify", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5356, "dossier_id": "heriot-watt/alana/length-model-1m", "name": "Alana length ranker (1M)", "maker": "heriot-watt", "family": "heriot-watt/alana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5357, "dossier_id": "heriot-watt/alana/rating-model", "name": "Alana rating ranker", "maker": "heriot-watt", "family": "heriot-watt/alana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5358, "dossier_id": "heriot-watt/emma/emma", "name": "EMMA", "maker": "heriot-watt", "family": "heriot-watt/emma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5359, "dossier_id": "heygen/avatar/avatar-iii", "name": "Avatar III", "maker": "heygen", "family": "heygen/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5360, "dossier_id": "heygen/avatar/avatar-iii-legacy", "name": "Avatar III (legacy v1/v2)", "maker": "heygen", "family": "heygen/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5361, "dossier_id": "heygen/avatar/avatar-iv", "name": "Avatar IV", "maker": "heygen", "family": "heygen/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5362, "dossier_id": "heygen/avatar/avatar-v", "name": "Avatar V", "maker": "heygen", "family": "heygen/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5363, "dossier_id": "heygen/image/heygen-image-1", "name": "HeyGen Image", "maker": "heygen", "family": "heygen/image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5364, "dossier_id": "heygen/video/heygen-video-1", "name": "HeyGen Video", "maker": "heygen", "family": "heygen/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5365, "dossier_id": "heygen/voice/heygen-voice-1", "name": "HeyGen Voice", "maker": "heygen", "family": "heygen/voice", "context_length": null, "prompt_price_per_m": 30.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5366, "dossier_id": "heygen/voice/heygen-voice-1-turbo", "name": "HeyGen Voice 1 Turbo", "maker": "heygen", "family": "heygen/voice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5367, "dossier_id": "heygen/voice/starfish", "name": "Starfish", "maker": "heygen", "family": "heygen/voice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5368, "dossier_id": "hiroshima/seismic/ehvr-gmm", "name": "EHVR-GMM", "maker": "hiroshima", "family": "hiroshima/seismic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5369, "dossier_id": "hiroshima/seismic/mhvr-saf", "name": "MHVR-to-SAF", "maker": "hiroshima", "family": "hiroshima/seismic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5370, "dossier_id": "hiroshima/seismic/mhvr-saf-tl", "name": "MHVR-to-SAF-TL", "maker": "hiroshima", "family": "hiroshima/seismic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5371, "dossier_id": "hit/abacus/abacus", "name": "\u73e0\u7b97 Abacus", "maker": "hit", "family": "hit/abacus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5372, "dossier_id": "hit/abacus/abacus-fim", "name": "\u73e0\u7b97 Abacus-FIM", "maker": "hit", "family": "hit/abacus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5373, "dossier_id": "hit/chinese-mixtral/chinese-mixtral-8x7b", "name": "Chinese-Mixtral-8x7B", "maker": "hit", "family": "hit/chinese-mixtral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5374, "dossier_id": "hit/glyphbert/glyphbert", "name": "GlyphBERT", "maker": "hit", "family": "hit/glyphbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5375, "dossier_id": "hit/hfl/chinese-lert-base", "name": "Chinese LERT-base", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5376, "dossier_id": "hit/hfl/chinese-lert-large", "name": "Chinese LERT-large", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5377, "dossier_id": "hit/hfl/chinese-lert-small", "name": "Chinese LERT-small", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5378, "dossier_id": "hit/hfl/chinese-pert-base", "name": "Chinese PERT-base", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5379, "dossier_id": "hit/hfl/chinese-pert-large", "name": "Chinese PERT-large", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5380, "dossier_id": "hit/hfl/cino-base-v2", "name": "CINO-base-v2", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CINO-base-v2 is the v2 CINO encoder from HFL for Chinese minority languages. Collected 2026-10-15. Apache-2.0.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5381, "dossier_id": "hit/hfl/cino-large-v2", "name": "CINO-large-v2", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CINO-large-v2 is the v2 CINO encoder from HFL for Chinese minority languages. Collected 2026-10-15. Apache-2.0.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5382, "dossier_id": "hit/hfl/cino-small-v2", "name": "CINO-small-v2", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CINO-small-v2 is the v2 CINO encoder from HFL for Chinese minority languages. Collected 2026-10-15. Apache-2.0.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5383, "dossier_id": "hit/hfl/english-pert-base", "name": "English PERT-base", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5384, "dossier_id": "hit/hfl/english-pert-large", "name": "English PERT-large", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5385, "dossier_id": "hit/hfl/macbert-base", "name": "Chinese MacBERT-base", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5386, "dossier_id": "hit/hfl/macbert-large", "name": "Chinese MacBERT-large", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5387, "dossier_id": "hit/hfl/rbt3", "name": "RBT3", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5388, "dossier_id": "hit/hfl/rbt4", "name": "RBT4", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Another small RoBERTa-wwm-ext-family encoder. Size details beyond the name were not in the download table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5389, "dossier_id": "hit/hfl/rbtl3", "name": "RBTL3", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5390, "dossier_id": "hit/hfl/vle-base", "name": "VLE-base", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Discriminative image-text tasks. The card says VCR, which needs high-level language understanding, is where VLE improves most.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5391, "dossier_id": "hit/hfl/vle-large", "name": "VLE-large", "maker": "hit", "family": "hit/hfl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Discriminative image-text tasks. The card says VCR, which needs high-level language understanding, is where VLE improves most.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5392, "dossier_id": "hit/huozi/huozi-1-0", "name": "\u6d3b\u5b57 1.0", "maker": "hit", "family": "hit/huozi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5393, "dossier_id": "hit/huozi/huozi-2-0", "name": "\u6d3b\u5b57 2.0", "maker": "hit", "family": "hit/huozi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5394, "dossier_id": "hit/kalm/kalm-embedding-multilingual-mini-instruct-v1", "name": "KaLM-embedding-multilingual-mini-instruct-v1", "maker": "hit", "family": "hit/kalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5395, "dossier_id": "hit/kalm/kalm-embedding-multilingual-mini-instruct-v1-5", "name": "KaLM-embedding-multilingual-mini-instruct-v1.5", "maker": "hit", "family": "hit/kalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5396, "dossier_id": "hit/kalm/kalm-embedding-multilingual-mini-v1", "name": "KaLM-embedding-multilingual-mini-v1", "maker": "hit", "family": "hit/kalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5397, "dossier_id": "hit/ltp/ltp-base", "name": "LTP Base", "maker": "hit", "family": "hit/ltp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5398, "dossier_id": "hit/ltp/ltp-base1", "name": "LTP Base1", "maker": "hit", "family": "hit/ltp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5399, "dossier_id": "hit/ltp/ltp-base2", "name": "LTP Base2", "maker": "hit", "family": "hit/ltp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5400, "dossier_id": "hit/ltp/ltp-legacy", "name": "LTP Legacy", "maker": "hit", "family": "hit/ltp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5401, "dossier_id": "hit/ltp/ltp-small", "name": "LTP Small", "maker": "hit", "family": "hit/ltp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5402, "dossier_id": "hit/ltp/ltp-tiny", "name": "LTP Tiny", "maker": "hit", "family": "hit/ltp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5403, "dossier_id": "hit/lychee/lychee-fd", "name": "Lychee-FD", "maker": "hit", "family": "hit/lychee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5404, "dossier_id": "hit/lychee/lychee-rerank-mm", "name": "Lychee-rerank-mm", "maker": "hit", "family": "hit/lychee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5405, "dossier_id": "hit/uni-moe/uni-moe-2-0-base", "name": "Uni-MoE 2.0-Base", "maker": "hit", "family": "hit/uni-moe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5406, "dossier_id": "hit/uni-moe/uni-moe-2-0-image", "name": "Uni-MoE 2.0-Image", "maker": "hit", "family": "hit/uni-moe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5407, "dossier_id": "hit/uni-moe/uni-moe-2-0-omni", "name": "Uni-MoE 2.0-Omni", "maker": "hit", "family": "hit/uni-moe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5408, "dossier_id": "hit/uni-moe/uni-moe-2-0-thinking", "name": "Uni-MoE 2.0-Thinking", "maker": "hit", "family": "hit/uni-moe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5409, "dossier_id": "hit/uni-moe/uni-moe-tts", "name": "Uni-MoE-TTS", "maker": "hit", "family": "hit/uni-moe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5410, "dossier_id": "hit/uni-moe/unimoe-audio", "name": "UniMoE-Audio Preview", "maker": "hit", "family": "hit/uni-moe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5411, "dossier_id": "hitachi/citras/citras-fm", "name": "CITRAS-FM", "maker": "hitachi", "family": "hitachi/citras", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5412, "dossier_id": "hitachi/iwim/iwim", "name": "IWIM", "maker": "hitachi", "family": "hitachi/iwim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5413, "dossier_id": "hku/anygraph/anygraph", "name": "AnyGraph", "maker": "hku", "family": "hku/anygraph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5414, "dossier_id": "hku/opencity/opencity-base", "name": "OpenCity-Base", "maker": "hku", "family": "hku/opencity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5415, "dossier_id": "hku/opencity/opencity-mini", "name": "OpenCity-Mini", "maker": "hku", "family": "hku/opencity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5416, "dossier_id": "hku/opencity/opencity-plus", "name": "OpenCity-Plus", "maker": "hku", "family": "hku/opencity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5417, "dossier_id": "hku/opencua/opencua-32b", "name": "OpenCUA-32B", "maker": "hku", "family": "hku/opencua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5418, "dossier_id": "hku/opencua/opencua-72b", "name": "OpenCUA-72B", "maker": "hku", "family": "hku/opencua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5419, "dossier_id": "hku/recgpt/recgpt", "name": "RecGPT", "maker": "hku", "family": "hku/recgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5420, "dossier_id": "hku/reclm/reclm", "name": "RecLM", "maker": "hku", "family": "hku/reclm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5421, "dossier_id": "hku/videoagenttrek/videoagenttrek-screenfilter", "name": "VideoAgentTrek-ScreenFilter", "maker": "hku", "family": "hku/videoagenttrek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5422, "dossier_id": "hkust/audio-omni/audio-omni", "name": "Audio-Omni", "maker": "hkust", "family": "hkust/audio-omni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5423, "dossier_id": "hkust/audiox/audiox", "name": "AudioX", "maker": "hkust", "family": "hkust/audiox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5424, "dossier_id": "hkust/audiox/audiox-maf", "name": "AudioX-MAF", "maker": "hkust", "family": "hkust/audiox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5425, "dossier_id": "hkust/audiox/audiox-maf-mmdit", "name": "AudioX-MAF-MMDiT", "maker": "hkust", "family": "hkust/audiox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5426, "dossier_id": "hkust/audiox/audiox-turbo", "name": "AudioX-Turbo", "maker": "hkust", "family": "hkust/audiox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5427, "dossier_id": "hkust/codeio/deepseek-v2-lite-coder-codeio", "name": "DeepSeek-V2-Lite-Coder CodeI/O", "maker": "hkust", "family": "hkust/codeio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DeepSeek-V2-Lite-Coder CodeI/O in February 2025. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5428, "dossier_id": "hkust/codeio/deepseek-v2-lite-coder-codeio-pp", "name": "DeepSeek-V2-Lite-Coder CodeI/O++", "maker": "hkust", "family": "hkust/codeio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DeepSeek-V2-Lite-Coder CodeI/O++ in February 2025. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5429, "dossier_id": "hkust/codeio/llama-3-1-8b-codeio", "name": "Llama-3.1 8B CodeI/O", "maker": "hkust", "family": "hkust/codeio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released Llama-3.1 8B CodeI/O in February 2025. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5430, "dossier_id": "hkust/codeio/llama-3-1-8b-codeio-pp", "name": "Llama-3.1 8B CodeI/O++", "maker": "hkust", "family": "hkust/codeio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released Llama-3.1 8B CodeI/O++ in February 2025. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5431, "dossier_id": "hkust/codeio/qwen-2-5-7b-coder-codeio", "name": "Qwen2.5-Coder 7B CodeI/O", "maker": "hkust", "family": "hkust/codeio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released Qwen2.5-Coder 7B CodeI/O in February 2025. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5432, "dossier_id": "hkust/codeio/qwen-2-5-7b-coder-codeio-pp", "name": "Qwen2.5-Coder 7B CodeI/O++", "maker": "hkust", "family": "hkust/codeio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released Qwen2.5-Coder 7B CodeI/O++ in February 2025. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5433, "dossier_id": "hkust/dart-math/dart-math-dsmath-7b-prop2diff", "name": "DART-Math DeepSeekMath 7B Prop2Diff", "maker": "hkust", "family": "hkust/dart-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DART-Math DeepSeekMath 7B Prop2Diff, a public math fine-tune. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5434, "dossier_id": "hkust/dart-math/dart-math-dsmath-7b-uniform", "name": "DART-Math DeepSeekMath 7B Uniform", "maker": "hkust", "family": "hkust/dart-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DART-Math DeepSeekMath 7B Uniform, a public math fine-tune. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5435, "dossier_id": "hkust/dart-math/dart-math-llama3-70b-prop2diff", "name": "DART-Math Llama-3 70B Prop2Diff", "maker": "hkust", "family": "hkust/dart-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DART-Math Llama-3 70B Prop2Diff, a public math fine-tune. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5436, "dossier_id": "hkust/dart-math/dart-math-llama3-70b-uniform", "name": "DART-Math Llama-3 70B Uniform", "maker": "hkust", "family": "hkust/dart-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DART-Math Llama-3 70B Uniform, a public math fine-tune. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5437, "dossier_id": "hkust/dart-math/dart-math-llama3-8b-prop2diff", "name": "DART-Math Llama-3 8B Prop2Diff", "maker": "hkust", "family": "hkust/dart-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DART-Math Llama-3 8B Prop2Diff, a public math fine-tune. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5438, "dossier_id": "hkust/dart-math/dart-math-llama3-8b-uniform", "name": "DART-Math Llama-3 8B Uniform", "maker": "hkust", "family": "hkust/dart-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DART-Math Llama-3 8B Uniform, a public math fine-tune. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5439, "dossier_id": "hkust/dart-math/dart-math-mistral-7b-prop2diff", "name": "DART-Math Mistral 7B Prop2Diff", "maker": "hkust", "family": "hkust/dart-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DART-Math Mistral 7B Prop2Diff, a public math fine-tune. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5440, "dossier_id": "hkust/dart-math/dart-math-mistral-7b-uniform", "name": "DART-Math Mistral 7B Uniform", "maker": "hkust", "family": "hkust/dart-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released DART-Math Mistral 7B Uniform, a public math fine-tune. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5441, "dossier_id": "hkust/deita/deita-7b-v1-0", "name": "DEITA 7B v1.0", "maker": "hkust", "family": "hkust/deita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction model from HKUST NLP's DEITA data-selection project. The card says it is the SFT plus DPO release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5442, "dossier_id": "hkust/deita/deita-7b-v1-0-sft", "name": "DEITA 7B v1.0 SFT", "maker": "hkust", "family": "hkust/deita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Supervised-only DEITA 7B checkpoint, before the DPO step used in deita-7b-v1-0.", "summary_intro": "HKUST NLP released the SFT-only DEITA 7B checkpoint alongside the DPO model. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5443, "dossier_id": "hkust/deita/deita-complexity-scorer", "name": "DEITA Complexity Scorer", "maker": "hkust", "family": "hkust/deita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scores instruction complexity for DEITA data selection, paired with the quality scorer.", "summary_intro": "HKUST NLP released the DEITA complexity scorer next to the quality scorer. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5444, "dossier_id": "hkust/deita/deita-llama1-13b-v1-0-sft", "name": "DEITA LLaMA-1 13B v1.0 SFT", "maker": "hkust", "family": "hkust/deita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger LLaMA-1 DEITA instruction checkpoint. Card row uses 10K SFT data.", "summary_intro": "HKUST NLP released DEITA-LLaMA1-13B-v1.0-sft. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5445, "dossier_id": "hkust/deita/deita-llama2-13b-v1-0-sft", "name": "DEITA LLaMA-2 13B v1.0 SFT", "maker": "hkust", "family": "hkust/deita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "LLaMA-2 13B DEITA instruction checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5446, "dossier_id": "hkust/deita/deita-quality-scorer", "name": "DEITA Quality Scorer", "maker": "hkust", "family": "hkust/deita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scores instruction-response quality so DEITA can select SFT data.", "summary_intro": "HKUST NLP released the DEITA quality scorer, a Llama-1 13B annotator. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5447, "dossier_id": "hkust/drkernel/drkernel-14b", "name": "DR.Kernel 14B", "maker": "hkust", "family": "hkust/drkernel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generates and iteratively edits Triton GPU kernels from a PyTorch reference, using execution feedback. Not a general chat model.", "summary_intro": "HKUST NLP released DR.Kernel 14B on 2026-02-05 for Triton kernel generation. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5448, "dossier_id": "hkust/drkernel/drkernel-8b", "name": "DR.Kernel 8B", "maker": "hkust", "family": "hkust/drkernel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generates and iteratively edits Triton GPU kernels from a PyTorch reference, using execution feedback. Not a general chat model.", "summary_intro": "HKUST NLP released DR.Kernel 8B on 2026-02-05 for Triton kernel generation. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5449, "dossier_id": "hkust/graph-r1/graph-r1-1-5b", "name": "Graph-R1 1.5B", "maker": "hkust", "family": "hkust/graph-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST-DSAIL released Graph-R1 1.5B. Collected on 2026-10-15. Ablation checkpoints were not given folders.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5450, "dossier_id": "hkust/graph-r1/graph-r1-7b", "name": "Graph-R1 7B", "maker": "hkust", "family": "hkust/graph-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST-DSAIL released Graph-R1 7B. Collected on 2026-10-15. Ablation checkpoints were not given folders.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5451, "dossier_id": "hkust/graphmind/graphmind-gemma2-2b", "name": "GraphMind Gemma-2 2B", "maker": "hkust", "family": "hkust/graphmind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "HKUST(GZ) DSAIL models meant to carry graph reasoning over to math, logic, code, and commonsense tasks.", "summary_intro": "HKUST-DSAIL released GraphMind Gemma-2 2B in August 2025. Collected on 2026-10-15. The lab is at HKUST (Guangzhou).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5452, "dossier_id": "hkust/graphmind/graphmind-llama-3-1-8b", "name": "GraphMind Llama-3.1 8B", "maker": "hkust", "family": "hkust/graphmind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "HKUST(GZ) DSAIL models meant to carry graph reasoning over to math, logic, code, and commonsense tasks.", "summary_intro": "HKUST-DSAIL released GraphMind Llama-3.1 8B in August 2025. Collected on 2026-10-15. The lab is at HKUST (Guangzhou).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5453, "dossier_id": "hkust/graphmind/graphmind-llama-3-8b", "name": "GraphMind Llama-3 8B", "maker": "hkust", "family": "hkust/graphmind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "HKUST(GZ) DSAIL models meant to carry graph reasoning over to math, logic, code, and commonsense tasks.", "summary_intro": "HKUST-DSAIL released GraphMind Llama-3 8B in August 2025. Collected on 2026-10-15. The lab is at HKUST (Guangzhou).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5454, "dossier_id": "hkust/hk-o1aw/hk-o1aw", "name": "HK-O1aw", "maker": "hkust", "family": "hkust/hk-o1aw", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKAIR-Lab at HKUST released HK-O1aw v0 on 2024-10-30. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5455, "dossier_id": "hkust/jailjudge/jailjudge-guard", "name": "JailJudge Guard", "maker": "hkust", "family": "hkust/jailjudge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "usail-hkust released JailJudge Guard on 2024-09-30. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5456, "dossier_id": "hkust/llasa/llasa-1b", "name": "Llasa 1B", "maker": "hkust", "family": "hkust/llasa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5457, "dossier_id": "hkust/llasa/llasa-1b-multilingual", "name": "Llasa 1B Multilingual", "maker": "hkust", "family": "hkust/llasa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5458, "dossier_id": "hkust/llasa/llasa-3b", "name": "Llasa 3B", "maker": "hkust", "family": "hkust/llasa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5459, "dossier_id": "hkust/llasa/llasa-8b", "name": "Llasa 8B", "maker": "hkust", "family": "hkust/llasa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5460, "dossier_id": "hkust/llmlight/llmlight-lightgpt", "name": "LLMLight LightGPT", "maker": "hkust", "family": "hkust/llmlight", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says these are the weights from the LLMLight paper, which uses language models as traffic-signal control agents.", "summary_intro": "usail-hkust released LLMLight-LightGPT on 2024-02-09. Collected on 2026-10-15. HKUST (Guangzhou) tag is on the card.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5461, "dossier_id": "hkust/mstar/mstar-8b", "name": "M-STAR 8B", "maker": "hkust", "family": "hkust/mstar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multimodal math reasoner. The card says the M-STAR model scores 59.5 on MathVista.", "summary_intro": "HKUST NLP released M-STAR 8B, a MiniCPM-V reasoning model, on 2024-12-25. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5462, "dossier_id": "hkust/mstar/mstar-prm-8b", "name": "M-STAR PRM 8B", "maker": "hkust", "family": "hkust/mstar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Step-level judge for multimodal reasoning traces in the M-STAR pipeline.", "summary_intro": "HKUST NLP released the M-STAR process reward model with the 8B reasoner. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5463, "dossier_id": "hkust/preselect/preselect-fasttext", "name": "PreSelect fastText classifier", "maker": "hkust", "family": "hkust/preselect", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Filters pretraining text. The paper is Predictive Data Selection. It is a released model, not only a script.", "summary_intro": "HKUST NLP released the PreSelect fastText classifier used to build PreSelect-100B. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5464, "dossier_id": "hkust/ser/ser-eng-zho-adults", "name": "SER wav2vec2 English-Chinese adults", "maker": "hkust", "family": "hkust/ser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CAiRE at HKUST released SER wav2vec2 English-Chinese adults on 2023-06-27. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5465, "dossier_id": "hkust/ser/ser-eng-zho-all-age", "name": "SER wav2vec2 English-Chinese all ages", "maker": "hkust", "family": "hkust/ser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CAiRE at HKUST released SER wav2vec2 English-Chinese all ages on 2023-06-27. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5466, "dossier_id": "hkust/ser/ser-eng-zho-elderly", "name": "SER wav2vec2 English-Chinese elderly", "maker": "hkust", "family": "hkust/ser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CAiRE at HKUST released SER wav2vec2 English-Chinese elderly on 2023-06-27. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5467, "dossier_id": "hkust/simplerl/deepseek-math-7b-simplerl-zoo", "name": "DeepSeekMath 7B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "summary_intro": "HKUST NLP released DeepSeekMath 7B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5468, "dossier_id": "hkust/simplerl/llama-3-1-8b-simplerl-zoo", "name": "Llama-3.1 8B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5469, "dossier_id": "hkust/simplerl/mistral-7b-v0-1-simplerl-zoo", "name": "Mistral 7B v0.1 SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5470, "dossier_id": "hkust/simplerl/mistral-small-24b-simplerl-zoo", "name": "Mistral Small 24B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "summary_intro": "HKUST NLP released Mistral Small 24B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5471, "dossier_id": "hkust/simplerl/qwen-2-5-0-5b-simplerl-zoo", "name": "Qwen2.5 0.5B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5472, "dossier_id": "hkust/simplerl/qwen-2-5-1-5b-simplerl-zoo", "name": "Qwen2.5 1.5B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5473, "dossier_id": "hkust/simplerl/qwen-2-5-14b-simplerl-zoo", "name": "Qwen2.5 14B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "14B member of the ten-model zoo. The GitHub score row was not fully captured, so scores are left empty.", "summary_intro": "HKUST NLP released Qwen2.5 14B SimpleRL-Zoo with the SimpleRL-Zoo drop. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5474, "dossier_id": "hkust/simplerl/qwen-2-5-32b-simplerl-zoo", "name": "Qwen2.5 32B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "32B member of the ten-model zoo. Score row not captured.", "summary_intro": "HKUST NLP released Qwen2.5 32B SimpleRL-Zoo with the SimpleRL-Zoo drop. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5475, "dossier_id": "hkust/simplerl/qwen-2-5-7b-simplerl-zoo", "name": "Qwen2.5 7B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One of the ten SimpleRL-Zoo checkpoints. HKUST NLP trains zero RL directly from open base models.", "summary_intro": "HKUST NLP released Qwen2.5 7B SimpleRL-Zoo on 2025-03-24 as part of SimpleRL-Zoo. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5476, "dossier_id": "hkust/simplerl/qwen-2-5-math-7b-simplerl", "name": "Qwen2.5-Math 7B SimpleRL", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Paired February release with SimpleRL-Zero. The lab's news post tells them apart from the later zoo.", "summary_intro": "HKUST NLP released Qwen2.5-Math-7B-SimpleRL on 2025-02-18. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5477, "dossier_id": "hkust/simplerl/qwen-2-5-math-7b-simplerl-zero", "name": "Qwen2.5-Math 7B SimpleRL-Zero", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Early SimpleRL checkpoint. The card says training starts from the base model with 8K MATH examples.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5478, "dossier_id": "hkust/simplerl/qwen-2-5-math-7b-simplerl-zoo", "name": "Qwen2.5-Math 7B SimpleRL-Zoo", "maker": "hkust", "family": "hkust/simplerl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "HKUST NLP released Qwen2.5-Math 7B SimpleRL-Zoo with the SimpleRL-Zoo drop. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5479, "dossier_id": "hkust/talker/talker-t2av", "name": "Talker-T2AV", "maker": "hkust", "family": "hkust/talker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says Talker-T2AV jointly generates talking audio and video with autoregressive diffusion.", "summary_intro": "HKUST Audio released Talker-T2AV, a talking audio-video model, under Apache-2.0. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5480, "dossier_id": "hkust/univar/univar-lambda-1", "name": "UniVaR lambda 1", "maker": "hkust", "family": "hkust/univar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "CAiRE HKUST embedding checkpoints for sentence similarity. Each lambda is a separate trained checkpoint with its own MTEB card dump.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5481, "dossier_id": "hkust/univar/univar-lambda-20", "name": "UniVaR lambda 20", "maker": "hkust", "family": "hkust/univar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "CAiRE HKUST embedding checkpoints for sentence similarity. Each lambda is a separate trained checkpoint with its own MTEB card dump.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5482, "dossier_id": "hkust/univar/univar-lambda-5", "name": "UniVaR lambda 5", "maker": "hkust", "family": "hkust/univar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "CAiRE HKUST embedding checkpoints for sentence similarity. Each lambda is a separate trained checkpoint with its own MTEB card dump.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5483, "dossier_id": "hkust/univar/univar-lambda-80", "name": "UniVaR lambda 80", "maker": "hkust", "family": "hkust/univar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "CAiRE HKUST embedding checkpoints for sentence similarity. Each lambda is a separate trained checkpoint with its own MTEB card dump.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5484, "dossier_id": "hkust/urbankgent/urbankgent-13b", "name": "UrbanKGent 13B", "maker": "hkust", "family": "hkust/urbankgent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "usail-hkust released UrbanKGent 13B on 2024-10-04. Collected on 2026-10-15. Authors are at HKUST (Guangzhou) and HKUST.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5485, "dossier_id": "hkust/urbankgent/urbankgent-7b", "name": "UrbanKGent 7B", "maker": "hkust", "family": "hkust/urbankgent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "usail-hkust released UrbanKGent 7B on 2024-10-04. Collected on 2026-10-15. Authors are at HKUST (Guangzhou) and HKUST.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5486, "dossier_id": "hkust/urbankgent/urbankgent-8b", "name": "UrbanKGent 8B", "maker": "hkust", "family": "hkust/urbankgent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "usail-hkust released UrbanKGent 8B on 2024-10-04. Collected on 2026-10-15. Authors are at HKUST (Guangzhou) and HKUST.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5487, "dossier_id": "hkust/vibeworlder/vibeworlder-8b", "name": "VibeWorlder 8B", "maker": "hkust", "family": "hkust/vibeworlder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5488, "dossier_id": "hkust/vibeworlder/vibeworlder-embedding-4b", "name": "VibeWorlder Embedding 4B", "maker": "hkust", "family": "hkust/vibeworlder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embedding model released with VibeWorlder. The card does not say what it embeds.", "summary_intro": "usail-hkust released VibeWorlder-Embedding-4B on 2026-08-06. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5489, "dossier_id": "hkust/vidmuse/vidmuse", "name": "VidMuse", "maker": "hkust", "family": "hkust/vidmuse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says VidMuse generates music aligned with a video using long-short-term modeling. Accepted to CVPR 2025.", "summary_intro": "HKUST Audio released VidMuse, a video-to-music model. Collected on 2026-10-15. License CC-BY-4.0.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5490, "dossier_id": "hkust/wav2vec2/wav2vec2-large-xlsr-53-cantonese", "name": "Wav2Vec2-Large-XLSR-53 Cantonese", "maker": "hkust", "family": "hkust/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cantonese speech recognition from CAiRE HKUST. The card reports test CER 18.55 percent on Common Voice zh-HK.", "summary_intro": "CAiRE at HKUST released a Cantonese wav2vec2 ASR model on 2022-04-09. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5491, "dossier_id": "hkust/xcodec/xcodec2", "name": "XCodec2", "maker": "hkust", "family": "hkust/xcodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5492, "dossier_id": "hkust/yue/yue-s1-0-5b", "name": "YuE S1 0.5B", "maker": "hkust", "family": "hkust/yue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A smaller public YuE stage-1 checkpoint released after the 7B anneals, under Apache-2.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5493, "dossier_id": "hkust/yue/yue-s1-7b", "name": "YuE S1 7B", "maker": "hkust", "family": "hkust/yue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5494, "dossier_id": "hkust/yue/yue-s2-1b", "name": "YuE S2 1B", "maker": "hkust", "family": "hkust/yue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5495, "dossier_id": "hkust/yue/yue-upsampler", "name": "YuE Upsampler", "maker": "hkust", "family": "hkust/yue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The YuE project table lists an upsampler beside S1 and S2. It is a separate weight repo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5496, "dossier_id": "hkust/yue/yue2-3b", "name": "YuE2 3B", "maker": "hkust", "family": "hkust/yue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5497, "dossier_id": "hokkaido/ainu-mt/byt5-small-ain-jpn-mt", "name": "ByT5-small Ainu to Japanese", "maker": "hokkaido", "family": "hokkaido/ainu-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5498, "dossier_id": "hokkaido/ainu-mt/byt5-small-jpn-ain-mt", "name": "ByT5-small Japanese to Ainu", "maker": "hokkaido", "family": "hokkaido/ainu-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5499, "dossier_id": "hokkaido/ainu-mt/mt5-small-ain-jpn-mt", "name": "mT5-small Ainu to Japanese", "maker": "hokkaido", "family": "hokkaido/ainu-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5500, "dossier_id": "hokkaido/ainu-mt/mt5-small-jpn-ain-mt", "name": "mT5-small Japanese to Ainu", "maker": "hokkaido", "family": "hokkaido/ainu-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5501, "dossier_id": "honor/magicagent/magicagent-30b-a3b", "name": "MagicAgent-30B-A3B", "maker": "honor", "family": "honor/magicagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5502, "dossier_id": "honor/magicagent/magicagent-32b", "name": "MagicAgent-32B", "maker": "honor", "family": "honor/magicagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5503, "dossier_id": "honor/magicagent/magicagent-plan", "name": "MagicAgent-Plan", "maker": "honor", "family": "honor/magicagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5504, "dossier_id": "honor/magicagent/magicagent-tool", "name": "MagicAgent-Tool", "maker": "honor", "family": "honor/magicagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5505, "dossier_id": "honor/magicagent/magicagent-ultra", "name": "MagicAgent-Ultra", "maker": "honor", "family": "honor/magicagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5506, "dossier_id": "honor/magicasr/magicasr", "name": "MagicASR", "maker": "honor", "family": "honor/magicasr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5507, "dossier_id": "honor/magicedit/magicedit", "name": "MagicEdit", "maker": "honor", "family": "honor/magicedit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The only job implied by the category label is visual editing in the cloud. The article does not list features.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5508, "dossier_id": "honor/magicgui/magicgui", "name": "MagicGUI", "maker": "honor", "family": "honor/magicgui", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5509, "dossier_id": "honor/magicgui/magicgui-action", "name": "MagicGUI-Action", "maker": "honor", "family": "honor/magicgui", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5510, "dossier_id": "honor/magicgui/magicgui-auto", "name": "MagicGUI-Auto", "maker": "honor", "family": "honor/magicgui", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5511, "dossier_id": "honor/magicimage/magicimage", "name": "MagicImage", "maker": "honor", "family": "honor/magicimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5512, "dossier_id": "honor/magiclm/magiclm", "name": "MagicLM\uff08\u9b54\u6cd5\u5927\u6a21\u578b\uff09", "maker": "honor", "family": "honor/magiclm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5513, "dossier_id": "honor/magiclm/magiclm-nano", "name": "MagicLM-Nano", "maker": "honor", "family": "honor/magiclm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5514, "dossier_id": "honor/magicspeech/magicspeech", "name": "MagicSpeech", "maker": "honor", "family": "honor/magicspeech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5515, "dossier_id": "honor/magictts/magictts", "name": "MagicTTS", "maker": "honor", "family": "honor/magictts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5516, "dossier_id": "honor/magicvideo/magicvideo", "name": "MagicVideo", "maker": "honor", "family": "honor/magicvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5517, "dossier_id": "honor/magicvl/magicvl-2b", "name": "MagicVL-2B", "maker": "honor", "family": "honor/magicvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5518, "dossier_id": "honor/magicvl/magicvl-nano", "name": "MagicVL-Nano", "maker": "honor", "family": "honor/magicvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5519, "dossier_id": "hourone/avatar/cinematic-avatar", "name": "Custom cinematic avatar", "maker": "hourone", "family": "hourone/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5520, "dossier_id": "hourone/avatar/custom-studio-avatar", "name": "Custom studio avatar", "maker": "hourone", "family": "hourone/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5521, "dossier_id": "hourone/avatar/desk-avatar", "name": "Desk Avatar", "maker": "hourone", "family": "hourone/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5522, "dossier_id": "hourone/avatar/stock-studio-avatar", "name": "Stock studio avatars", "maker": "hourone", "family": "hourone/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5523, "dossier_id": "hourone/avatar/web-avatar", "name": "Web avatar", "maker": "hourone", "family": "hourone/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5524, "dossier_id": "hourone/reals/reals", "name": "Reals", "maker": "hourone", "family": "hourone/reals", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5525, "dossier_id": "hu-berlin/entity/entity-english-4class", "name": "Flair 2.0 English NER 4-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents it as an NER tagger for Flair. Load SequenceTagger with the Hub id. PER, LOC, ORG, MISC.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5526, "dossier_id": "hu-berlin/entity/entity-english-8class", "name": "Flair 2.0 English NER 8-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5527, "dossier_id": "hu-berlin/entity/entity-german-4class", "name": "Flair 2.0 German NER 4-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents it as an NER tagger for Flair. Load SequenceTagger with the Hub id. PER, LOC, ORG, MISC.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5528, "dossier_id": "hu-berlin/entity/entity-german-8class", "name": "Flair 2.0 German NER 8-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5529, "dossier_id": "hu-berlin/entity/entity-multilingual-4class", "name": "Flair 2.0 multilingual NER 4-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5530, "dossier_id": "hu-berlin/entity/entity-multilingual-8class", "name": "Flair 2.0 multilingual NER 8-class", "maker": "hu-berlin", "family": "hu-berlin/entity", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5531, "dossier_id": "hu-berlin/finerweb/finerweb-binary-classifier-mdeberta-4o", "name": "finerweb-binary-classifier-mdeberta-4o", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5532, "dossier_id": "hu-berlin/finerweb/finerweb-binary-classifier-mdeberta-gemma3", "name": "finerweb-binary-classifier-mdeberta-gemma3", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5533, "dossier_id": "hu-berlin/finerweb/finerweb-binary-classifier-xlmr-4o", "name": "finerweb-binary-classifier-xlmr-4o", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5534, "dossier_id": "hu-berlin/finerweb/finerweb-binary-classifier-xlmr-gemma3", "name": "finerweb-binary-classifier-xlmr-gemma3", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5535, "dossier_id": "hu-berlin/finerweb/finerweb-multilabel-classifier-mdeberta-4o", "name": "finerweb-multilabel-classifier-mdeberta-4o", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5536, "dossier_id": "hu-berlin/finerweb/finerweb-multilabel-classifier-mdeberta-gemma3", "name": "finerweb-multilabel-classifier-mdeberta-gemma3", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5537, "dossier_id": "hu-berlin/finerweb/finerweb-multilabel-classifier-xlmr-4o", "name": "finerweb-multilabel-classifier-xlmr-4o", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5538, "dossier_id": "hu-berlin/finerweb/finerweb-multilabel-classifier-xlmr-gemma3", "name": "finerweb-multilabel-classifier-xlmr-gemma3", "maker": "hu-berlin", "family": "hu-berlin/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5539, "dossier_id": "hu-berlin/flair/chunk-english", "name": "Flair chunk English", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `chunk` for chunking on CoNLL-2000.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5540, "dossier_id": "hu-berlin/flair/chunk-english-fast", "name": "Flair chunk English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `chunk-fast` for chunking on CoNLL-2000.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5541, "dossier_id": "hu-berlin/flair/de-offensive-language", "name": "Flair German offensive language", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `de-offensive-language` for classification on GermEval 2018 Task 1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5542, "dossier_id": "hu-berlin/flair/de-pos", "name": "Flair POS German", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `de-pos` for pos on UD German HDT.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5543, "dossier_id": "hu-berlin/flair/de-pos-fine-grained", "name": "Flair German fine-grained POS", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `de-pos-fine-grained` for pos.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5544, "dossier_id": "hu-berlin/flair/de-pos-tweets", "name": "Flair POS German tweets", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `de-pos-tweets` for pos on German Tweets.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5545, "dossier_id": "hu-berlin/flair/frame-english", "name": "Flair frame English", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `frame` for frame on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5546, "dossier_id": "hu-berlin/flair/frame-english-fast", "name": "Flair frame English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `frame-fast` for frame on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5547, "dossier_id": "hu-berlin/flair/ner-danish", "name": "Flair NER Danish", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `da-ner` for ner on Danish NER / DaNE.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5548, "dossier_id": "hu-berlin/flair/ner-dutch", "name": "Flair NER Dutch", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `nl-ner` for ner on CoNLL 2002.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5549, "dossier_id": "hu-berlin/flair/ner-dutch-large", "name": "Flair NER Dutch large", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `nl-ner-large` for ner on CoNLL-03 (docs wording).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5550, "dossier_id": "hu-berlin/flair/ner-dutch-rnn", "name": "Flair NER Dutch RNN", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `nl-ner-rnn` for ner on CoNLL 2002.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5551, "dossier_id": "hu-berlin/flair/ner-english", "name": "Flair NER English", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner` for ner on CoNLL-03.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5552, "dossier_id": "hu-berlin/flair/ner-english-fast", "name": "Flair NER English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner-fast` for ner on CoNLL-03.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5553, "dossier_id": "hu-berlin/flair/ner-english-large", "name": "Flair NER English large", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner-large` for ner on CoNLL-03.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5554, "dossier_id": "hu-berlin/flair/ner-english-ontonotes", "name": "Flair NER English Ontonotes", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner-ontonotes` for ner on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5555, "dossier_id": "hu-berlin/flair/ner-english-ontonotes-fast", "name": "Flair NER English Ontonotes fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner-ontonotes-fast` for ner on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5556, "dossier_id": "hu-berlin/flair/ner-english-ontonotes-large", "name": "Flair NER English Ontonotes large", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner-ontonotes-large` for ner on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5557, "dossier_id": "hu-berlin/flair/ner-french", "name": "Flair NER French", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `fr-ner` for ner on WikiNER aij-wikiner-fr-wp3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5558, "dossier_id": "hu-berlin/flair/ner-german", "name": "Flair NER German", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `de-ner` for ner on CoNLL-03.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5559, "dossier_id": "hu-berlin/flair/ner-german-germeval", "name": "Flair NER German GermEval", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `de-ner-germeval` for ner on GermEval.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5560, "dossier_id": "hu-berlin/flair/ner-german-large", "name": "Flair NER German large", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `de-ner-large` for ner on CoNLL-03.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5561, "dossier_id": "hu-berlin/flair/ner-german-legal", "name": "Flair NER German legal", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `de-ner-legal` for ner on LER.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5562, "dossier_id": "hu-berlin/flair/ner-multi", "name": "Flair NER multilingual", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner-multi` for ner on CoNLL-03.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5563, "dossier_id": "hu-berlin/flair/ner-multi-fast", "name": "Flair NER multilingual fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner-multi-fast` for ner on CoNLL-03.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5564, "dossier_id": "hu-berlin/flair/ner-pooled", "name": "Flair NER English pooled", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `ner-pooled` for ner on CoNLL-03.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5565, "dossier_id": "hu-berlin/flair/ner-spanish-large", "name": "Flair NER Spanish large", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `es-ner-large` for ner on CoNLL-03 Spanish.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5566, "dossier_id": "hu-berlin/flair/pos-english", "name": "Flair POS English", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `pos` for pos on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5567, "dossier_id": "hu-berlin/flair/pos-english-fast", "name": "Flair POS English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `pos-fast` for pos on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5568, "dossier_id": "hu-berlin/flair/sentiment", "name": "Flair sentiment English", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `sentiment` for sentiment on movie and product reviews.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5569, "dossier_id": "hu-berlin/flair/sentiment-fast", "name": "Flair sentiment English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `sentiment-fast` for sentiment on movie and product reviews.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5570, "dossier_id": "hu-berlin/flair/upos-english", "name": "Flair UPOS English", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `upos` for pos on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5571, "dossier_id": "hu-berlin/flair/upos-english-fast", "name": "Flair UPOS English fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `upos-fast` for pos on Ontonotes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5572, "dossier_id": "hu-berlin/flair/upos-multi", "name": "Flair UPOS multilingual", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `pos-multi` for pos on UD treebanks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5573, "dossier_id": "hu-berlin/flair/upos-multi-fast", "name": "Flair UPOS multilingual fast", "maker": "hu-berlin", "family": "hu-berlin/flair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Flair docs tell users to load `pos-multi-fast` for pos on UD treebanks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5574, "dossier_id": "hu-berlin/flukes/fluke-ner-en-7class", "name": "Fluke NER English 7-class", "maker": "hu-berlin", "family": "hu-berlin/flukes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5575, "dossier_id": "hu-berlin/hunflair/hunflair", "name": "HunFlair", "maker": "hu-berlin", "family": "hu-berlin/hunflair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5576, "dossier_id": "hu-berlin/hunflair/hunflair2", "name": "HunFlair2", "maker": "hu-berlin", "family": "hu-berlin/hunflair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5577, "dossier_id": "hu-berlin/opiniongpt/opiniongpt", "name": "OpinionGPT", "maker": "hu-berlin", "family": "hu-berlin/opiniongpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5578, "dossier_id": "hu-berlin/otter/otter-bi-mmbert", "name": "Otter bi-encoder mmBERT", "maker": "hu-berlin", "family": "hu-berlin/otter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5579, "dossier_id": "hu-berlin/otter/otter-bi-rembert", "name": "Otter bi-encoder RemBERT", "maker": "hu-berlin", "family": "hu-berlin/otter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5580, "dossier_id": "hu-berlin/otter/otter-cross-mmbert", "name": "Otter cross-encoder mmBERT", "maker": "hu-berlin", "family": "hu-berlin/otter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5581, "dossier_id": "hu-berlin/otter/otter-cross-rembert", "name": "Otter cross-encoder RemBERT", "maker": "hu-berlin", "family": "hu-berlin/otter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5582, "dossier_id": "huawei/cemat/cemat", "name": "CeMAT", "maker": "huawei", "family": "huawei/cemat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual pretraining for neural machine translation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5583, "dossier_id": "huawei/nezha/nezha", "name": "NEZHA", "maker": "huawei", "family": "huawei/nezha", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese language-understanding pretrained model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5584, "dossier_id": "huawei/nezha/nezha-gen", "name": "NEZHA-Gen", "maker": "huawei", "family": "huawei/nezha", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese GPT generation, including a general model and the Yuefu poetry model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5585, "dossier_id": "huawei/nezha/yuefu", "name": "Yuefu", "maker": "huawei", "family": "huawei/nezha", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classical Chinese poetry generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5586, "dossier_id": "huawei/openpangu/openpangu-7b-diffusion-base", "name": "openPangu-7B-Diffusion-Base", "maker": "huawei", "family": "huawei/openpangu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named openPangu diffusion language model. Specs were not read from the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5587, "dossier_id": "huawei/openpangu/openpangu-7b-diffusion-deepdiver", "name": "openPangu-7B-Diffusion-DeepDiver", "maker": "huawei", "family": "huawei/openpangu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named openPangu diffusion language model. Specs were not read from the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5588, "dossier_id": "huawei/openpangu/openpangu-embedded-7b-deepdiver", "name": "openPangu-Embedded-7B-DeepDiver", "maker": "huawei", "family": "huawei/openpangu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named DeepDiver variant of the embedded 7B line. Behavior was not read from a card in this pass.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5589, "dossier_id": "huawei/openpangu/openpangu-r-7b-diffusion", "name": "openPangu-R-7B-Diffusion", "maker": "huawei", "family": "huawei/openpangu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named openPangu diffusion language model. Specs were not read from the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5590, "dossier_id": "huawei/openpangu/openpangu-ultra-moe-718b", "name": "openPangu-Ultra-MoE-718B", "maker": "huawei", "family": "huawei/openpangu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flagship open-weight MoE language model with fused fast and slow thinking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5591, "dossier_id": "huawei/openpangu/pangu-pro-moe", "name": "Pangu Pro MoE", "maker": "huawei", "family": "huawei/openpangu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "72B-class grouped-expert MoE optimized for Ascend inference. First widely downloaded open Pangu weight in June 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5592, "dossier_id": "huawei/pangu-ai4s/pangu-ai4s-global-precip", "name": "Pangu-AI4S-Global-Precip", "maker": "huawei", "family": "huawei/pangu-ai4s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Global precipitation forecast.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5593, "dossier_id": "huawei/pangu-ai4s/pangu-ai4s-global-weather", "name": "Pangu-AI4S-Global-Weather", "maker": "huawei", "family": "huawei/pangu-ai4s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Forecasts basic global weather elements. Time resolution is the smallest forecast step after data are fed in.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5594, "dossier_id": "huawei/pangu-ai4s/pangu-ai4s-global-wind", "name": "Pangu-AI4S-Global-Wind", "maker": "huawei", "family": "huawei/pangu-ai4s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Global new-energy wind elements.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5595, "dossier_id": "huawei/pangu-ai4s/pangu-ai4s-regional-photovoltaics", "name": "Pangu-AI4S-Regional-Photovoltaics", "maker": "huawei", "family": "huawei/pangu-ai4s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Regional new-energy solar elements.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5596, "dossier_id": "huawei/pangu-ai4s/pangu-ai4s-regional-precip", "name": "Pangu-AI4S-Regional-Precip", "maker": "huawei", "family": "huawei/pangu-ai4s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Regional precipitation forecast.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5597, "dossier_id": "huawei/pangu-ai4s/pangu-ai4s-regional-weather", "name": "Pangu-AI4S-Regional-Weather", "maker": "huawei", "family": "huawei/pangu-ai4s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Regional medium-range weather elements.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5598, "dossier_id": "huawei/pangu-ai4s/pangu-ai4s-regional-wind", "name": "Pangu-AI4S-Regional-Wind", "maker": "huawei", "family": "huawei/pangu-ai4s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Regional new-energy wind elements.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5599, "dossier_id": "huawei/pangu-ai4s/pangu-weather", "name": "Pangu-Weather", "maker": "huawei", "family": "huawei/pangu-ai4s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medium-range global weather forecasting, the research model behind the later AI4S weather SKUs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5600, "dossier_id": "huawei/pangu-alpha/pangu-alpha", "name": "PanGu-\u03b1", "maker": "huawei", "family": "huawei/pangu-alpha", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese autoregressive pretraining, the first named Pangu language model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5601, "dossier_id": "huawei/pangu-coder/pangu-coder", "name": "PanGu-Coder", "maker": "huawei", "family": "huawei/pangu-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation model that PanGu-Coder2 compares against.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5602, "dossier_id": "huawei/pangu-coder/pangu-coder2", "name": "PanGu-Coder2", "maker": "huawei", "family": "huawei/pangu-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "15B code model trained with the paper's ranking-feedback method.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5603, "dossier_id": "huawei/pangu-cv/pangu-cv-anomaly-v3", "name": "Pangu CV anomaly detection V3", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Finds points, events, or observations that do not match the expected pattern.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5604, "dossier_id": "huawei/pangu-cv/pangu-cv-image-classify-v2", "name": "Pangu CV image classification V2", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classification head on the unified Pangu pretrained vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5605, "dossier_id": "huawei/pangu-cv/pangu-cv-instance-seg-v1", "name": "Pangu CV instance segmentation V1", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instance segmentation head on the unified pretrained backbone. The page's wording also says a classification head.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5606, "dossier_id": "huawei/pangu-cv/pangu-cv-interactive-detect-v3", "name": "Pangu CV interactive detection V3", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Detects targets from visual prompts or text prompts, and can be fine-tuned on scene data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5607, "dossier_id": "huawei/pangu-cv/pangu-cv-object-detect-n-v2", "name": "Pangu CV object detection N V2", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Object detector in the tens-of-millions to hundreds-of-millions parameter range. Supports model extraction. Pretrained on Huawei data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5608, "dossier_id": "huawei/pangu-cv/pangu-cv-object-detect-s-v2", "name": "Pangu CV object detection S V2", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small-parameter object detector pretrained on Huawei's own image data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5609, "dossier_id": "huawei/pangu-cv/pangu-cv-object-detect-s-v3", "name": "Pangu CV object detection S V3", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Optimized for accuracy, latency, and small objects. The page calls it a high price-performance detector.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5610, "dossier_id": "huawei/pangu-cv/pangu-cv-open-set-classify-v3", "name": "Pangu CV open-set classification V3", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Assigns categories from image features, including categories beyond a closed training set. Edge deploy is not supported.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5611, "dossier_id": "huawei/pangu-cv/pangu-cv-open-vocab-detect-v2", "name": "Pangu CV open-vocabulary detection V2", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Detects known and unknown categories and is described as able to respond to new categories.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5612, "dossier_id": "huawei/pangu-cv/pangu-cv-open-vocab-seg-v2", "name": "Pangu CV open-vocabulary segmentation V2", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Segments known and unknown categories.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5613, "dossier_id": "huawei/pangu-cv/pangu-cv-semantic-seg-v2", "name": "Pangu CV semantic segmentation V2", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Segmentation head on the unified Pangu pretrained vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5614, "dossier_id": "huawei/pangu-cv/pangu-cv-tracking-v3", "name": "Pangu CV tracking V3", "maker": "huawei", "family": "huawei/pangu-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Detects, extracts, recognizes, and tracks an object of interest in video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5615, "dossier_id": "huawei/pangu-mm/pangu-mm-m1-txt2img", "name": "Pangu-MM-M1-Txt2Img", "maker": "huawei", "family": "huawei/pangu-mm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image at 1024 by 1024, with several named styles.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5616, "dossier_id": "huawei/pangu-mm/pangu-mm-m2-aigvideo", "name": "Pangu-MM-M2-AIGVideo", "maker": "huawei", "family": "huawei/pangu-mm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-video and short video continuation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5617, "dossier_id": "huawei/pangu-mm/pangu-mm-m2-text2video", "name": "Pangu-MM-M2-Text2Video", "maker": "huawei", "family": "huawei/pangu-mm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-video generation, 5 seconds, five aspect ratios.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5618, "dossier_id": "huawei/pangu-pi/pangu-pi-1-5b-pro", "name": "PanGu-\u03c0-1.5B Pro", "maker": "huawei", "family": "huawei/pangu-pi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.5B tiny language model in the PanGu-\u03c0 Pro pair.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5619, "dossier_id": "huawei/pangu-pi/pangu-pi-1b", "name": "PanGu-\u03c0-1B", "maker": "huawei", "family": "huawei/pangu-pi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1B research model used to show the PanGu-\u03c0 nonlinearity changes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5620, "dossier_id": "huawei/pangu-pi/pangu-pi-1b-pro", "name": "PanGu-\u03c0-1B Pro", "maker": "huawei", "family": "huawei/pangu-pi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1B tiny language model aimed at tighter device budgets.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5621, "dossier_id": "huawei/pangu-pi/pangu-pi-7b", "name": "PanGu-\u03c0-7B", "maker": "huawei", "family": "huawei/pangu-pi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B research model, later used as the base of the YunShan finance and law model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5622, "dossier_id": "huawei/pangu-pi/yunshan", "name": "YunShan", "maker": "huawei", "family": "huawei/pangu-pi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Finance and law model built by deploying PanGu-\u03c0-7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5623, "dossier_id": "huawei/pangu-predict/pangu-predict-anom-table", "name": "Pangu-Predict-Anom-Table-2.0.2", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tabular anomaly detection by classifying normal versus abnormal, with a confidence score.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5624, "dossier_id": "huawei/pangu-predict/pangu-predict-anom-ts", "name": "Pangu-Predict-Anom-TS-3.0.0", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Time-series anomaly detection. Pretraining on normal history, then adapters for thresholds or labeled binary detection.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5625, "dossier_id": "huawei/pangu-predict/pangu-predict-anom-ts-v2", "name": "Pangu-Predict-Anom-TS-3.1.0", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot anomaly scores on the V2 time-series base. Point, interval, trend, and periodic anomalies. No fine-tune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5626, "dossier_id": "huawei/pangu-predict/pangu-predict-cla-table", "name": "Pangu-Predict-Cla-Table", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tabular classification. Versions differ by base model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5627, "dossier_id": "huawei/pangu-predict/pangu-predict-cla-ts", "name": "Pangu-Predict-Cla-TS-3.0.1", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Time-series classification on a pretrained forecast base. Discrete target states. Fine-tune, covariates, incremental training. Cloud and edge.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5628, "dossier_id": "huawei/pangu-predict/pangu-predict-fewshot-table", "name": "Pangu-Predict-FewShot-Table-3.0.4", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Direct prediction for small tables under 20,000 rows. Regression and classification. Tabular-regression fine-tune is open.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5629, "dossier_id": "huawei/pangu-predict/pangu-predict-industry-demand", "name": "Pangu-Predict-Industry-Demand", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hierarchical supply-chain demand forecasting.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5630, "dossier_id": "huawei/pangu-predict/pangu-predict-industry-heating", "name": "Pangu-Predict-Industry-Heating-V1.0.0", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Heating-industry model. Present on the operations and resource tables, absent from spec table 1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5631, "dossier_id": "huawei/pangu-predict/pangu-predict-industry-traffic", "name": "Pangu-Predict-Industry-Traffic-V3.0.0", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Highway flow and speed regression. Multi-site, multi-target, multi-scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5632, "dossier_id": "huawei/pangu-predict/pangu-predict-reg-table", "name": "Pangu-Predict-Reg-Table", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tabular regression. Versions differ by base model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5633, "dossier_id": "huawei/pangu-predict/pangu-predict-reg-ts", "name": "Pangu-Predict-Reg-TS-3.1.0", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Time-series regression of continuous values, up to 128 future points. Multi-target, incremental training, covariates.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5634, "dossier_id": "huawei/pangu-predict/pangu-predict-reg-zeroshot-ts", "name": "Pangu-Predict-Reg-ZeroShot-TS-3.0.0", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot time-series regression. Quantiles, multi-channel, categorical columns, dynamic covariates. Up to 1024 future points. No fine-tune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5635, "dossier_id": "huawei/pangu-predict/pangu-predict-structure", "name": "Pangu-Predict-StructurePredict-1.1.0", "maker": "huawei", "family": "huawei/pangu-predict", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Structured tabular classification and regression. Region on the spec table is \u4e0a\u6d77\u4e00 only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5636, "dossier_id": "huawei/pangu-pro/pangu-embeddingrank-zh", "name": "Pangu-EmbeddingRank-zh", "maker": "huawei", "family": "huawei/pangu-pro", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese document vectors and reranking for RAG.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5637, "dossier_id": "huawei/pangu-sigma/pangu-sigma", "name": "PanGu-\u03a3", "maker": "huawei", "family": "huawei/pangu-sigma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5638, "dossier_id": "huawei/tinybert/tinybert", "name": "TinyBERT", "maker": "huawei", "family": "huawei/tinybert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller, faster BERT for language understanding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5639, "dossier_id": "huawei/wukong/wukong", "name": "WuKong", "maker": "huawei", "family": "huawei/wukong", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese vision-language benchmarking models trained on the WuKong dataset.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5640, "dossier_id": "huggingface/alignment/mistral-7b-anthropic", "name": "Mistral-7B-Anthropic style", "maker": "huggingface", "family": "huggingface/alignment", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face H4 experiment that applies a constitutional-AI-style conversation mixture to Mistral-7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5641, "dossier_id": "huggingface/alignment/mistral-7b-grok", "name": "Mistral-7B-Grok style", "maker": "huggingface", "family": "huggingface/alignment", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face H4 Mistral-7B fine-tune aimed at a Grok-like conversational style, using a harmless-conversation set.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5642, "dossier_id": "huggingface/data-classifier/finemath-classifier", "name": "FineMath classifier", "maker": "huggingface", "family": "huggingface/data-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face classifier that scores mathematical educational quality of web text for FineMath.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5643, "dossier_id": "huggingface/data-classifier/python-edu-scorer", "name": "Python-Edu scorer", "maker": "huggingface", "family": "huggingface/data-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face scorer for educational Python used in SmolLM data curation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5644, "dossier_id": "huggingface/distil-whisper/distil-large-v2", "name": "Distil-Whisper large-v2", "maker": "huggingface", "family": "huggingface/distil-whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earlier large English distilled Whisper.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5645, "dossier_id": "huggingface/distil-whisper/distil-large-v3", "name": "Distil-Whisper large-v3", "maker": "huggingface", "family": "huggingface/distil-whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The README's recommended general checkpoint before v3.5, compatible with Whisper libraries.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5646, "dossier_id": "huggingface/distil-whisper/distil-large-v3-5", "name": "Distil-Whisper large-v3.5", "maker": "huggingface", "family": "huggingface/distil-whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 2025 English Distil-Whisper large model, trained to be a more robust drop-in than distil-large-v3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5647, "dossier_id": "huggingface/distil-whisper/distil-medium-en", "name": "Distil-Whisper medium.en", "maker": "huggingface", "family": "huggingface/distil-whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English mid-size distilled Whisper.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5648, "dossier_id": "huggingface/distil-whisper/distil-small-en", "name": "Distil-Whisper small.en", "maker": "huggingface", "family": "huggingface/distil-whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5649, "dossier_id": "huggingface/idefics/idefics-80b", "name": "IDEFICS-80B", "maker": "huggingface", "family": "huggingface/idefics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 80B base IDEFICS model, Hugging Face's open Flamingo-style vision-language model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5650, "dossier_id": "huggingface/idefics/idefics-80b-instruct", "name": "IDEFICS-80B-Instruct", "maker": "huggingface", "family": "huggingface/idefics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-tuned 80B IDEFICS for visual question answering and chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5651, "dossier_id": "huggingface/smolvla/smolvla", "name": "SmolVLA", "maker": "huggingface", "family": "huggingface/smolvla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face's compact robotics vision-language-action model for community robot data and consumer hardware.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5652, "dossier_id": "huggingface/stack-edu/stack-edu-c", "name": "Stack-Edu c classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for c, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5653, "dossier_id": "huggingface/stack-edu/stack-edu-cpp", "name": "Stack-Edu cpp classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for cpp, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5654, "dossier_id": "huggingface/stack-edu/stack-edu-csharp", "name": "Stack-Edu csharp classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for csharp, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5655, "dossier_id": "huggingface/stack-edu/stack-edu-go", "name": "Stack-Edu go classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for go, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5656, "dossier_id": "huggingface/stack-edu/stack-edu-java", "name": "Stack-Edu java classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for java, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5657, "dossier_id": "huggingface/stack-edu/stack-edu-javascript", "name": "Stack-Edu javascript classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for javascript, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5658, "dossier_id": "huggingface/stack-edu/stack-edu-markdown", "name": "Stack-Edu markdown classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for markdown, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5659, "dossier_id": "huggingface/stack-edu/stack-edu-php", "name": "Stack-Edu php classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for php, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5660, "dossier_id": "huggingface/stack-edu/stack-edu-python", "name": "Stack-Edu python classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for python, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5661, "dossier_id": "huggingface/stack-edu/stack-edu-ruby", "name": "Stack-Edu ruby classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for ruby, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5662, "dossier_id": "huggingface/stack-edu/stack-edu-rust", "name": "Stack-Edu rust classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for rust, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5663, "dossier_id": "huggingface/stack-edu/stack-edu-shell", "name": "Stack-Edu shell classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for shell, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5664, "dossier_id": "huggingface/stack-edu/stack-edu-sql", "name": "Stack-Edu sql classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for sql, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5665, "dossier_id": "huggingface/stack-edu/stack-edu-swift", "name": "Stack-Edu swift classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for swift, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5666, "dossier_id": "huggingface/stack-edu/stack-edu-typescript", "name": "Stack-Edu typescript classifier", "maker": "huggingface", "family": "huggingface/stack-edu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Educational-quality classifier for typescript, used to filter code pretraining data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5667, "dossier_id": "huggingface/starchat/starchat-alpha", "name": "StarChat-alpha", "maker": "huggingface", "family": "huggingface/starchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First Hugging Face H4 coding assistant fine-tune in the StarChat line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5668, "dossier_id": "huggingface/websight/vlm-websight", "name": "VLM WebSight", "maker": "huggingface", "family": "huggingface/websight", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hugging Face M4 vision-language model fine-tuned on the WebSight dataset and tagged for code generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5669, "dossier_id": "huji/hebtts/hebtts", "name": "HebTTS", "maker": "huji", "family": "huji/hebtts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5670, "dossier_id": "huji/lothm/lothm", "name": "LoTHM", "maker": "huji", "family": "huji/lothm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5671, "dossier_id": "huji/nast/nast", "name": "NAST", "maker": "huji", "family": "huji/nast", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5672, "dossier_id": "huji/nast/nast-ulm", "name": "NAST-ULM", "maker": "huji", "family": "huji/nast", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5673, "dossier_id": "huji/past/past", "name": "PAST", "maker": "huji", "family": "huji/past", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5674, "dossier_id": "huji/stresslm/stresslm", "name": "StresSLM", "maker": "huji", "family": "huji/stresslm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5675, "dossier_id": "huji/whistress/whistress", "name": "WhiStress", "maker": "huji", "family": "huji/whistress", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5676, "dossier_id": "hume/ellm/ellm", "name": "eLLM", "maker": "hume", "family": "hume/ellm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5677, "dossier_id": "hume/evi/evi-1", "name": "EVI 1", "maker": "hume", "family": "hume/evi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5678, "dossier_id": "hume/evi/evi-2", "name": "EVI 2", "maker": "hume", "family": "hume/evi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5679, "dossier_id": "hume/evi/evi-2-large", "name": "EVI-2-large", "maker": "hume", "family": "hume/evi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5680, "dossier_id": "hume/evi/evi-3", "name": "EVI 3", "maker": "hume", "family": "hume/evi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5681, "dossier_id": "hume/evi/evi-4-mini", "name": "EVI 4-mini", "maker": "hume", "family": "hume/evi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5682, "dossier_id": "hume/evi/hume-evi-2", "name": "hume-evi-2", "maker": "hume", "family": "hume/evi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5683, "dossier_id": "hume/evi/hume-evi-3", "name": "hume-evi-3", "maker": "hume", "family": "hume/evi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5684, "dossier_id": "hume/evi/hume-evi-3-websearch", "name": "hume-evi-3-websearch", "maker": "hume", "family": "hume/evi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5685, "dossier_id": "hume/expression/emotional-language", "name": "Emotional language", "maker": "hume", "family": "hume/expression", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5686, "dossier_id": "hume/expression/prosody", "name": "Speech prosody", "maker": "hume", "family": "hume/expression", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5687, "dossier_id": "hume/expression/speech-emotion", "name": "Speech emotion model", "maker": "hume", "family": "hume/expression", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5688, "dossier_id": "hume/expression/vocal-burst", "name": "Vocal burst", "maker": "hume", "family": "hume/expression", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5689, "dossier_id": "hume/expression/voice-descriptor", "name": "Voice descriptor model", "maker": "hume", "family": "hume/expression", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5690, "dossier_id": "hume/octave/octave-1", "name": "Octave 1", "maker": "hume", "family": "hume/octave", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5691, "dossier_id": "hume/octave/octave-2", "name": "Octave 2", "maker": "hume", "family": "hume/octave", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5692, "dossier_id": "hume/octave/octave-3b", "name": "OCTAVE 3B", "maker": "hume", "family": "hume/octave", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5693, "dossier_id": "hume/tada/tada-1b", "name": "TADA 1B", "maker": "hume", "family": "hume/tada", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5694, "dossier_id": "hume/tada/tada-3b-ml", "name": "TADA 3B multilingual", "maker": "hume", "family": "hume/tada", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5695, "dossier_id": "hume/tada/tada-codec", "name": "TADA codec", "maker": "hume", "family": "hume/tada", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5696, "dossier_id": "hunan/360bev/360mapper", "name": "360Mapper", "maker": "hunan", "family": "hunan/360bev", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5697, "dossier_id": "hunan/cmnext/cmnext", "name": "CMNeXt", "maker": "hunan", "family": "hunan/cmnext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5698, "dossier_id": "hunan/esm-dbp/esm-dbp", "name": "ESM-DBP", "maker": "hunan", "family": "hunan/esm-dbp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5699, "dossier_id": "hunan/matchformer/matchformer", "name": "MatchFormer", "maker": "hunan", "family": "hunan/matchformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5700, "dossier_id": "hunan/onebev/onebev", "name": "OneBEV", "maker": "hunan", "family": "hunan/onebev", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5701, "dossier_id": "hunan/onebev/onebev-plus", "name": "OneBEV++", "maker": "hunan", "family": "hunan/onebev", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5702, "dossier_id": "hunan/trans4pass/trans4pass", "name": "Trans4PASS", "maker": "hunan", "family": "hunan/trans4pass", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5703, "dossier_id": "hunan/trans4pass/trans4pass-plus", "name": "Trans4PASS+", "maker": "hunan", "family": "hunan/trans4pass", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5704, "dossier_id": "hust/diffusiondrive/diffusiondrive", "name": "DiffusionDrive", "maker": "hust", "family": "hust/diffusiondrive", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5705, "dossier_id": "hust/diffusiondrive/diffusiondrivev2", "name": "DiffusionDriveV2", "maker": "hust", "family": "hust/diffusiondrive", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5706, "dossier_id": "hust/diffusionvl/diffusionvl-qwen2-5-7b", "name": "DiffusionVL-Qwen2.5-7B", "maker": "hust", "family": "hust/diffusionvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5707, "dossier_id": "hust/diffusionvl/diffusionvl-qwen2-5-vl-3b", "name": "DiffusionVL-Qwen2.5-VL-3B", "maker": "hust", "family": "hust/diffusionvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5708, "dossier_id": "hust/diffusionvl/diffusionvl-qwen2-5-vl-7b", "name": "DiffusionVL-Qwen2.5-VL-7B", "maker": "hust", "family": "hust/diffusionvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5709, "dossier_id": "hust/dreamwam/dreamwam", "name": "DreamWAM", "maker": "hust", "family": "hust/dreamwam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5710, "dossier_id": "hust/fasterwam/fasterwam", "name": "FasterWAM", "maker": "hust", "family": "hust/fasterwam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5711, "dossier_id": "hust/infinitevl/infinitevl", "name": "InfiniteVL-4B", "maker": "hust", "family": "hust/infinitevl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5712, "dossier_id": "hust/lightningdit/lightningdit-xl", "name": "LightningDiT-XL", "maker": "hust", "family": "hust/lightningdit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5713, "dossier_id": "hust/lkcell/lkcell-l", "name": "LKCell-L", "maker": "hust", "family": "hust/lkcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5714, "dossier_id": "hust/maptr/maptr", "name": "MapTR", "maker": "hust", "family": "hust/maptr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5715, "dossier_id": "hust/maptr/maptrv2", "name": "MapTRv2", "maker": "hust", "family": "hust/maptr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5716, "dossier_id": "hust/matvlm/matvlm", "name": "MaTVLM", "maker": "hust", "family": "hust/matvlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5717, "dossier_id": "hust/mim4d/mim4d", "name": "MIM4D", "maker": "hust", "family": "hust/mim4d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5718, "dossier_id": "hust/mmmamba/mmmamba-hybrid", "name": "mmMamba-hybrid", "maker": "hust", "family": "hust/mmmamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5719, "dossier_id": "hust/mmmamba/mmmamba-linear", "name": "mmMamba-linear", "maker": "hust", "family": "hust/mmmamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5720, "dossier_id": "hust/mobilei2v/mobilei2v", "name": "MobileI2V", "maker": "hust", "family": "hust/mobilei2v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5721, "dossier_id": "hust/moebius/moebius", "name": "Moebius", "maker": "hust", "family": "hust/moebius", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Moebius is HUST Vision Lab's open-weight lightweight image-inpainting model. Collected 2026-10-15. Weights are public under MIT.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5722, "dossier_id": "hust/motionvla/motionvla", "name": "MotionVLA", "maker": "hust", "family": "hust/motionvla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5723, "dossier_id": "hust/multimodal-flow/multimodal-flow", "name": "Multimodal Flow 1.6B", "maker": "hust", "family": "hust/multimodal-flow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5724, "dossier_id": "hust/omnimamba/omnimamba", "name": "OmniMamba", "maker": "hust", "family": "hust/omnimamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5725, "dossier_id": "hust/pixelhacker/pixelhacker", "name": "PixelHacker", "maker": "hust", "family": "hust/pixelhacker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "PixelHacker is HUST Vision Lab's open-weight image-inpainting model. Collected 2026-10-15. Weights are public under MIT.", "summary_intro": "PixelHacker is HUST Vision Lab's open-weight image-inpainting model. Collected 2026-10-15. Weights are public under MIT.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5726, "dossier_id": "hust/spa3-vlm/spa3-vlm", "name": "Spa3-VLM", "maker": "hust", "family": "hust/spa3-vlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5727, "dossier_id": "hust/spa3r/spa3r", "name": "Spa3R", "maker": "hust", "family": "hust/spa3r", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5728, "dossier_id": "hust/superclip/superclip-b", "name": "SuperCLIP-B", "maker": "hust", "family": "hust/superclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5729, "dossier_id": "hust/superclip/superclip-l", "name": "SuperCLIP-L", "maker": "hust", "family": "hust/superclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5730, "dossier_id": "hust/turbo-vaed/turbo-vaed", "name": "Turbo-VAED", "maker": "hust", "family": "hust/turbo-vaed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5731, "dossier_id": "hust/va-vae/va-vae", "name": "VA-VAE", "maker": "hust", "family": "hust/va-vae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5732, "dossier_id": "hust/vad/vad-base", "name": "VAD-Base", "maker": "hust", "family": "hust/vad", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5733, "dossier_id": "hust/vad/vad-tiny", "name": "VAD-Tiny", "maker": "hust", "family": "hust/vad", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5734, "dossier_id": "hust/vgt/vgt-ae", "name": "VGT autoencoder", "maker": "hust", "family": "hust/vgt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5735, "dossier_id": "hust/vgt/vgt-internvl3-1-6b", "name": "VGT-InternVL3-1.6B", "maker": "hust", "family": "hust/vgt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5736, "dossier_id": "hust/vgt/vgt-qwen2-5-vl-1-6b", "name": "VGT-Qwen2.5-VL-1.6B", "maker": "hust", "family": "hust/vgt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5737, "dossier_id": "hust/vig/vig", "name": "ViG", "maker": "hust", "family": "hust/vig", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5738, "dossier_id": "hust/vim/vim-base-midclstok", "name": "Vim-Base middle class token", "maker": "hust", "family": "hust/vim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5739, "dossier_id": "hust/vim/vim-small-midclstok", "name": "Vim-Small middle class token", "maker": "hust", "family": "hust/vim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5740, "dossier_id": "hust/vim/vim-tiny", "name": "Vim-Tiny", "maker": "hust", "family": "hust/vim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5741, "dossier_id": "hust/vim/vim-tiny-midclstok", "name": "Vim-Tiny middle class token", "maker": "hust", "family": "hust/vim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5742, "dossier_id": "hust/vitmatte/vitmatte-base-composition-1k", "name": "ViTMatte-Base Composition-1k", "maker": "hust", "family": "hust/vitmatte", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5743, "dossier_id": "hust/vitmatte/vitmatte-base-distinctions-646", "name": "ViTMatte-Base Distinctions-646", "maker": "hust", "family": "hust/vitmatte", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5744, "dossier_id": "hust/vitmatte/vitmatte-small-composition-1k", "name": "ViTMatte-Small Composition-1k", "maker": "hust", "family": "hust/vitmatte", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5745, "dossier_id": "hust/vitmatte/vitmatte-small-distinctions-646", "name": "ViTMatte-Small Distinctions-646", "maker": "hust", "family": "hust/vitmatte", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5746, "dossier_id": "hust/yolos/yolos-base", "name": "YOLOS-Base", "maker": "hust", "family": "hust/yolos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5747, "dossier_id": "hust/yolos/yolos-small", "name": "YOLOS-Small", "maker": "hust", "family": "hust/yolos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5748, "dossier_id": "hust/yolos/yolos-small-300", "name": "YOLOS-Small (300 pre-train epochs)", "maker": "hust", "family": "hust/yolos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5749, "dossier_id": "hust/yolos/yolos-small-dwr", "name": "YOLOS-Small (fast model scaling)", "maker": "hust", "family": "hust/yolos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5750, "dossier_id": "hust/yolos/yolos-tiny", "name": "YOLOS-Tiny", "maker": "hust", "family": "hust/yolos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5751, "dossier_id": "hyper3d/rodin/rodin-bang", "name": "Rodin BANG", "maker": "hyper3d", "family": "hyper3d/rodin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5752, "dossier_id": "hyper3d/rodin/rodin-gen-1", "name": "Rodin Gen-1", "maker": "hyper3d", "family": "hyper3d/rodin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5753, "dossier_id": "hyper3d/rodin/rodin-gen-1-5", "name": "Rodin Gen-1.5", "maker": "hyper3d", "family": "hyper3d/rodin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5754, "dossier_id": "hyper3d/rodin/rodin-gen-2", "name": "Rodin Gen-2", "maker": "hyper3d", "family": "hyper3d/rodin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5755, "dossier_id": "hyper3d/rodin/rodin-gen-2-5", "name": "Rodin Gen-2.5", "maker": "hyper3d", "family": "hyper3d/rodin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5756, "dossier_id": "iastate/insectnet/insectnet", "name": "InsectNet", "maker": "iastate", "family": "iastate/insectnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5757, "dossier_id": "iastate/weednet/weednet", "name": "WeedNet", "maker": "iastate", "family": "iastate/weednet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5758, "dossier_id": "iastate/xplnet/xplnet", "name": "xPLNet", "maker": "iastate", "family": "iastate/xplnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5759, "dossier_id": "ibm/granite/granite-13b-chat-v1", "name": "Granite 13B Chat v1", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Granite 13B Chat v1 is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "summary_intro": "Granite 13B Chat v1 is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5760, "dossier_id": "ibm/granite/granite-13b-instruct-v1", "name": "Granite 13B Instruct v1", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Granite 13B Instruct v1 is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "summary_intro": "Granite 13B Instruct v1 is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5761, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5762, "dossier_id": "ibm/granite/granite-3-2-8b-alora-jailbreak", "name": "Granite 3.2 8B Instruct - Jailbreak aLoRA", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5763, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5764, "dossier_id": "ibm/granite/granite-3-2-8b-alora-requirement-check", "name": "Granite 3.2 8B Instruct - Requirement Checker", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5765, "dossier_id": "ibm/granite/granite-3-2-8b-lora-jailbreak", "name": "Granite 3.2 8B Instruct - Jailbreak LoRA", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5766, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5767, "dossier_id": "ibm/granite/granite-3-2-8b-lora-uncertainty", "name": "Granite 3.2 8B Instruct - Uncertainty LoRA", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5768, "dossier_id": "ibm/granite/granite-3-3-8b-alora-requirement-check", "name": "Granite 3.3 8B Instruct - Requirement Checker", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5769, "dossier_id": "ibm/granite/granite-3-3-8b-alora-uncertainty", "name": "Granite 3.3 8B Instruct - Uncertainty aLoRA", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5770, "dossier_id": "ibm/granite/granite-3-3-8b-lora-math-prm", "name": "Granite-3.3-8B-LoRA-Math-PRM", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5771, "dossier_id": "ibm/granite/granite-3-3-8b-rag-agent-lib", "name": "Granite 3.3 RAG Agent Library", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5772, "dossier_id": "ibm/granite/granite-3-3-8b-security-lib", "name": "Granite 3.3 Security Library", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5773, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5774, "dossier_id": "ibm/granite/granite-8b-japanese", "name": "Granite 8B Japanese", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": 0.6, "completion_price_per_m": 0.6, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Granite 8B Japanese is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "summary_intro": "Granite 8B Japanese is an IBM granite model. Collection date 2026-10-15. Status: deprecated.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5775, "dossier_id": "ibm/granite/granite-rag-3-0-8b-lora", "name": "Granite RAG 3.0 8b", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5776, "dossier_id": "ibm/granite/granite-uncertainty-3-0-8b-lora", "name": "Granite 3.0 8B Instruct - Uncertainty LoRA", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5777, "dossier_id": "ibm/granite/granitelib-core-r1-0", "name": "Granite Core Library", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5778, "dossier_id": "ibm/granite/granitelib-rag-r1-0", "name": "Granite RAG Library", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5779, "dossier_id": "ibm/granite/rag-intrinsics-lib", "name": "rag-intrinsics-lib", "maker": "ibm", "family": "ibm/granite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5780, "dossier_id": "ibm/granite-code/granite-20b-code-base-schema-linking", "name": "Granite 20B Code Base Schema Linking", "maker": "ibm", "family": "ibm/granite-code", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Granite 20B Code Base Schema Linking is an IBM granite code model. Collection date 2026-10-15. Status: active.", "summary_intro": "Granite 20B Code Base Schema Linking is an IBM granite code model. Collection date 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5781, "dossier_id": "ibm/granite-code/granite-20b-code-base-sql-gen", "name": "Granite 20B Code Base SQL Gen", "maker": "ibm", "family": "ibm/granite-code", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5782, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5783, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5784, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5785, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5786, "dossier_id": "ibm/granite-geospatial/granite-geospatial-biomass", "name": "Model Card for granite-geospatial-biomass", "maker": "ibm", "family": "ibm/granite-geospatial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5787, "dossier_id": "ibm/granite-geospatial/granite-geospatial-canopyheight", "name": "Model Card for granite-geospatial-canopy-height", "maker": "ibm", "family": "ibm/granite-geospatial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5788, "dossier_id": "ibm/granite-geospatial/granite-geospatial-land-surface-temperature", "name": "Model Card for granite-geospatial-land-surface-temperature", "maker": "ibm", "family": "ibm/granite-geospatial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5789, "dossier_id": "ibm/granite-geospatial/granite-geospatial-ocean", "name": "granite-geospatial-ocean", "maker": "ibm", "family": "ibm/granite-geospatial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5790, "dossier_id": "ibm/granite-geospatial/granite-geospatial-uki", "name": "Model Card for granite-geospatial-uki", "maker": "ibm", "family": "ibm/granite-geospatial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5791, "dossier_id": "ibm/granite-geospatial/granite-geospatial-uki-flooddetection", "name": "Model Card for granite-geospatial-flood-detection-uki", "maker": "ibm", "family": "ibm/granite-geospatial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5792, "dossier_id": "ibm/granite-geospatial/granite-geospatial-wxc-downscaling", "name": "Model card for granite-geospatial-wxc-downscaling", "maker": "ibm", "family": "ibm/granite-geospatial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5793, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5794, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5795, "dossier_id": "ibm/granite-guardian/granitelib-guardian-r1-0", "name": "Granite Guardian Library", "maker": "ibm", "family": "ibm/granite-guardian", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5796, "dossier_id": "ibm/granite-speech/granite-4-0-1b-speech", "name": "Granite-4.0-1b-speech", "maker": "ibm", "family": "ibm/granite-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5797, "dossier_id": "ibm/granite-speech/granite-speech-3-2-8b", "name": "Granite-speech-3.2-8b", "maker": "ibm", "family": "ibm/granite-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5798, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5799, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5800, "dossier_id": "ibm/granite-speech/granite-speech-4-1-2b", "name": "Granite-Speech-4.1-2B", "maker": "ibm", "family": "ibm/granite-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5801, "dossier_id": "ibm/granite-speech/granite-speech-4-1-2b-nar", "name": "Granite-Speech-4.1-2B-NAR", "maker": "ibm", "family": "ibm/granite-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5802, "dossier_id": "ibm/granite-speech/granite-speech-4-1-2b-plus", "name": "Granite-Speech-4.1-2B-Plus", "maker": "ibm", "family": "ibm/granite-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5803, "dossier_id": "ibm/granite-speech/granite-speech-5-0-470m-turboctc", "name": "Granite-Speech-5.0-470M-TurboCTC", "maker": "ibm", "family": "ibm/granite-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5804, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5805, "dossier_id": "ibm/granite-timeseries/granite-timeseries-ensemble-r1", "name": "Granite Timeseries Ensemble R1", "maker": "ibm", "family": "ibm/granite-timeseries", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5806, "dossier_id": "ibm/slate/slate-125m-english-rtrvr-v2", "name": "Slate 125M English RTRVR v2", "maker": "ibm", "family": "ibm/slate", "context_length": null, "prompt_price_per_m": 0.106, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5807, "dossier_id": "ibm/slate/slate-30m-english-rtrvr-v2", "name": "Slate 30M English RTRVR v2", "maker": "ibm", "family": "ibm/slate", "context_length": null, "prompt_price_per_m": 0.106, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5808, "dossier_id": "ideogram/ideogram/ideogram-0-1", "name": "Ideogram 0.1", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5809, "dossier_id": "ideogram/ideogram/ideogram-1-0", "name": "Ideogram 1.0", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5810, "dossier_id": "ideogram/ideogram/ideogram-2-0", "name": "Ideogram 2.0", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5811, "dossier_id": "ideogram/ideogram/ideogram-2a", "name": "Ideogram 2a", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5812, "dossier_id": "ideogram/ideogram/ideogram-3-0", "name": "Ideogram 3.0", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5813, "dossier_id": "ideogram/ideogram/ideogram-3-0-transparent", "name": "Ideogram 3.0 Transparent", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5814, "dossier_id": "ideogram/ideogram/ideogram-4-0-transparent", "name": "Ideogram 4.0 Transparent", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5815, "dossier_id": "ideogram/ideogram/ideogram-4-5", "name": "Ideogram 4.5", "maker": "ideogram", "family": "ideogram/ideogram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5816, "dossier_id": "iflytek/medical/spark-healthcare-v3-5", "name": "\u661f\u706b\u533b\u7597\u5927\u6a21\u578b V3.5", "maker": "iflytek", "family": "iflytek/medical", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5817, "dossier_id": "iflytek/spark/spark-4-0-turbo", "name": "\u8baf\u98de\u661f\u706b V4.0 Turbo", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5818, "dossier_id": "iflytek/spark/spark-kjwx", "name": "\u79d1\u6280\u6587\u732e\u5927\u6a21\u578b", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "iFlytek lists it as an industry large model optimized for paper question answering and writing, metered on its own.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5819, "dossier_id": "iflytek/spark/spark-v1-0", "name": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V1.0", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5820, "dossier_id": "iflytek/spark/spark-v1-5", "name": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V1.5", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5821, "dossier_id": "iflytek/spark/spark-v2-0", "name": "\u8baf\u98de\u661f\u706b\u8ba4\u77e5\u5927\u6a21\u578b V2.0", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5822, "dossier_id": "iflytek/spark/spark-x1", "name": "\u8baf\u98de\u661f\u706b X1", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5823, "dossier_id": "iflytek/spark/spark-x1-5", "name": "\u8baf\u98de\u661f\u706b X1.5", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5824, "dossier_id": "iflytek/spark/spark-x2", "name": "\u8baf\u98de\u661f\u706b X2", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 3.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5825, "dossier_id": "iflytek/spark/spark-x2-5", "name": "\u8baf\u98de\u661f\u706b X2.5", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": 1.6, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5826, "dossier_id": "iflytek/spark/spark-x2-5-30b-a3b", "name": "Spark X2.5 30B-A3B", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5827, "dossier_id": "iflytek/spark/spark-x2-agent", "name": "Spark X2 Agent", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5828, "dossier_id": "iflytek/spark/spark-x2-flash", "name": "Spark X2-Flash", "maker": "iflytek", "family": "iflytek/spark", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": 2.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5829, "dossier_id": "iflytek/speech/spark-audio-1-0-preview", "name": "Spark-Audio-1.0-Preview", "maker": "iflytek", "family": "iflytek/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5830, "dossier_id": "iflytek/speech/spark-iat-multi", "name": "\u591a\u8bed\u79cd\u8bc6\u522b\u5927\u6a21\u578b", "maker": "iflytek", "family": "iflytek/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5831, "dossier_id": "iflytek/speech/spark-iat-zh", "name": "\u8bed\u97f3\u542c\u5199\u5927\u6a21\u578b\uff08\u4e2d\u82f1\uff09", "maker": "iflytek", "family": "iflytek/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5832, "dossier_id": "iflytek/speech/spark-smart-tts", "name": "\u8d85\u62df\u4eba\u8bed\u97f3\u5408\u6210", "maker": "iflytek", "family": "iflytek/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5833, "dossier_id": "iiith/bhashaverse/bhashaverse-llm", "name": "BhashaVerse LLM", "maker": "iiith", "family": "iiith/bhashaverse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5834, "dossier_id": "iiith/bhashaverse/onenmt-v3b", "name": "OneNMT v3b", "maker": "iiith", "family": "iiith/bhashaverse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5835, "dossier_id": "iiith/lid/indic-lang-detection", "name": "IIITH Indic Language Detection", "maker": "iiith", "family": "iiith/lid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5836, "dossier_id": "iiith/ner/iiith-ner", "name": "IIITH NER", "maker": "iiith", "family": "iiith/ner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5837, "dossier_id": "iiith/nmt/trans-v1", "name": "IIITH Trans V1", "maker": "iiith", "family": "iiith/nmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5838, "dossier_id": "iiith/ocr/bhasha-ocr", "name": "Bhasha OCR", "maker": "iiith", "family": "iiith/ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5839, "dossier_id": "iiith/ocr/handwritten-bhaasha", "name": "Bhaasha Handwritten OCR", "maker": "iiith", "family": "iiith/ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The catalog modality is handwritten text. It lists eight languages. It is not the printed-text row.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5840, "dossier_id": "iiith/ocr/scene-text", "name": "IIITH Scene-Text OCR", "maker": "iiith", "family": "iiith/ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5841, "dossier_id": "iisc/sravaani/sravaani-0-5-live", "name": "SraVaani 0.5 Live", "maker": "iisc", "family": "iisc/sravaani", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5842, "dossier_id": "iisc/sravaani/sravaani-1-0", "name": "SraVaani 1.0", "maker": "iisc", "family": "iisc/sravaani", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5843, "dossier_id": "iitb/nmt/trilingual-en-hi-mr-v1", "name": "IITB Trilingual EN-HI-MR v1", "maker": "iitb", "family": "iitb/nmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5844, "dossier_id": "iitb/shrutam/shrutam-hindi-asr-1-0", "name": "Shrutam Hindi ASR 1.0", "maker": "iitb", "family": "iitb/shrutam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5845, "dossier_id": "iitd/lipikar/assamese-ocr", "name": "Lipikar Assamese OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5846, "dossier_id": "iitd/lipikar/bengali-ocr", "name": "Lipikar Bengali OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5847, "dossier_id": "iitd/lipikar/bodo-ocr", "name": "Lipikar Bodo OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5848, "dossier_id": "iitd/lipikar/dogri-ocr", "name": "Lipikar Dogri OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5849, "dossier_id": "iitd/lipikar/english-ocr", "name": "Lipikar English OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5850, "dossier_id": "iitd/lipikar/gujarati-ocr", "name": "Lipikar Gujarati OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5851, "dossier_id": "iitd/lipikar/hindi-ocr", "name": "Lipikar Hindi OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5852, "dossier_id": "iitd/lipikar/kannada-ocr", "name": "Lipikar Kannada OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5853, "dossier_id": "iitd/lipikar/kashmiri-ocr", "name": "Lipikar Kashmiri OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5854, "dossier_id": "iitd/lipikar/konkani-ocr", "name": "Lipikar Konkani OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5855, "dossier_id": "iitd/lipikar/maithili-ocr", "name": "Lipikar Maithili OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5856, "dossier_id": "iitd/lipikar/malayalam-ocr", "name": "Lipikar Malayalam OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5857, "dossier_id": "iitd/lipikar/manipuri-ocr", "name": "Lipikar Manipuri OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5858, "dossier_id": "iitd/lipikar/marathi-ocr", "name": "Lipikar Marathi OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5859, "dossier_id": "iitd/lipikar/nepali-ocr", "name": "Lipikar Nepali OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5860, "dossier_id": "iitd/lipikar/oriya-ocr", "name": "Lipikar Oriya OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5861, "dossier_id": "iitd/lipikar/punjabi-ocr", "name": "Lipikar Punjabi OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5862, "dossier_id": "iitd/lipikar/sanskrit-ocr", "name": "Lipikar Sanskrit OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5863, "dossier_id": "iitd/lipikar/santali-asa-ocr", "name": "Lipikar Santali (asa script) OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5864, "dossier_id": "iitd/lipikar/santali-bn-ocr", "name": "Lipikar Santali (bn script) OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5865, "dossier_id": "iitd/lipikar/sindhi-ocr", "name": "Lipikar Sindhi OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5866, "dossier_id": "iitd/lipikar/tamil-ocr", "name": "Lipikar Tamil OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5867, "dossier_id": "iitd/lipikar/telugu-ocr", "name": "Lipikar Telugu OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5868, "dossier_id": "iitd/lipikar/unified-indic-ocr", "name": "UnifiedIndicOCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5869, "dossier_id": "iitd/lipikar/urdu-ocr", "name": "Lipikar Urdu OCR", "maker": "iitd", "family": "iitd/lipikar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5870, "dossier_id": "iitd/smriti/smriti", "name": "SMRITI", "maker": "iitd", "family": "iitd/smriti", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5871, "dossier_id": "iitg/kws/kws", "name": "IIT Guwahati KWS", "maker": "iitg", "family": "iitg/kws", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5872, "dossier_id": "iitgn/ansh/ansh-128k", "name": "Ansh-128k", "maker": "iitgn", "family": "iitgn/ansh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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\")`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5873, "dossier_id": "iitgn/ansh/ansh-160k", "name": "Ansh-160k", "maker": "iitgn", "family": "iitgn/ansh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5874, "dossier_id": "iitgn/ansh/ansh-256k", "name": "Ansh-256k", "maker": "iitgn", "family": "iitgn/ansh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5875, "dossier_id": "iitgn/ganga/ganga-en-hi-1b", "name": "Ganga-en-hi-1B", "maker": "iitgn", "family": "iitgn/ganga", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5876, "dossier_id": "iitj/indicphotoocr/indicphotoocr", "name": "IndicPhotoOCR", "maker": "iitj", "family": "iitj/indicphotoocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5877, "dossier_id": "iitj/matr/matr", "name": "MATR", "maker": "iitj", "family": "iitj/matr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5878, "dossier_id": "iitj/patentlmm/patentlmm", "name": "PatentLMM", "maker": "iitj", "family": "iitj/patentlmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5879, "dossier_id": "iitk/il-pcsr/il-pcsr", "name": "IL-PCSR", "maker": "iitk", "family": "iitk/il-pcsr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5880, "dossier_id": "iitm/indicbart/indicbart", "name": "IndicBART", "maker": "iitm", "family": "iitm/indicbart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5881, "dossier_id": "iitm/indicbart/indicbartss", "name": "IndicBARTSS", "maker": "iitm", "family": "iitm/indicbart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5882, "dossier_id": "iitm/indicbert/indic-bert", "name": "IndicBERT", "maker": "iitm", "family": "iitm/indicbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5883, "dossier_id": "iitm/indicbert/indicbert-v2", "name": "IndicBERTv2", "maker": "iitm", "family": "iitm/indicbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5884, "dossier_id": "iitm/indicconformer/indicconformer", "name": "IndicConformer", "maker": "iitm", "family": "iitm/indicconformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5885, "dossier_id": "iitm/indicconformer/indicconformer-600m-multilingual", "name": "IndicConformer-600M-Multi", "maker": "iitm", "family": "iitm/indicconformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5886, "dossier_id": "iitm/indicf5/indicf5", "name": "IndicF5", "maker": "iitm", "family": "iitm/indicf5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5887, "dossier_id": "iitm/indiclid/indiclid", "name": "IndicLID", "maker": "iitm", "family": "iitm/indiclid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5888, "dossier_id": "iitm/indictrans/indictrans", "name": "IndicTrans", "maker": "iitm", "family": "iitm/indictrans", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5889, "dossier_id": "iitm/indictrans2/indictrans2-en-indic-1b", "name": "IndicTrans2 En-Indic 1B", "maker": "iitm", "family": "iitm/indictrans2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5890, "dossier_id": "iitm/indictrans2/indictrans2-en-indic-dist-200m", "name": "IndicTrans2 En-Indic Distilled 200M", "maker": "iitm", "family": "iitm/indictrans2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5891, "dossier_id": "iitm/indictrans2/indictrans2-indic-en-1b", "name": "IndicTrans2 Indic-En 1B", "maker": "iitm", "family": "iitm/indictrans2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5892, "dossier_id": "iitm/indictrans2/indictrans2-indic-en-dist-200m", "name": "IndicTrans2 Indic-En Distilled 200M", "maker": "iitm", "family": "iitm/indictrans2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It translates the scheduled Indic languages into English at the distilled size. The 1B Indic-En model remains available.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5893, "dossier_id": "iitm/indictrans2/indictrans2-indic-indic-1b", "name": "IndicTrans2 Indic-Indic 1B", "maker": "iitm", "family": "iitm/indictrans2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5894, "dossier_id": "iitm/indictrans2/indictrans2-indic-indic-dist-320m", "name": "IndicTrans2 Indic-Indic Distilled 320M", "maker": "iitm", "family": "iitm/indictrans2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It is the distilled many-to-many Indic model. English is not a translation side.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5895, "dossier_id": "iitm/indicxlit/indicxlit", "name": "IndicXlit", "maker": "iitm", "family": "iitm/indicxlit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5896, "dossier_id": "iitmandi/chara/chara", "name": "Chara", "maker": "iitmandi", "family": "iitmandi/chara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5897, "dossier_id": "iitmandi/lid/audio-lang-detection", "name": "IIT Mandi Audio Language Detection", "maker": "iitmandi", "family": "iitmandi/lid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5898, "dossier_id": "iitmandi/s2a/s2a-net", "name": "Surface2Anatomy (S2A-Net)", "maker": "iitmandi", "family": "iitmandi/s2a", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5899, "dossier_id": "iitropar/gromo/mvvt", "name": "MVVT", "maker": "iitropar", "family": "iitropar/gromo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5900, "dossier_id": "imbue/imbue/imbue-70b", "name": "Imbue 70B", "maker": "imbue", "family": "imbue/imbue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5901, "dossier_id": "imperial/arc2face/arc2face", "name": "Arc2Face", "maker": "imperial", "family": "imperial/arc2face", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5902, "dossier_id": "imperial/arcface/arcface-mfn", "name": "ArcFace MobileFaceNet", "maker": "imperial", "family": "imperial/arcface", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5903, "dossier_id": "imperial/arcface/arcface-r100", "name": "ArcFace ResNet-100", "maker": "imperial", "family": "imperial/arcface", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5904, "dossier_id": "imperial/lsfm/lsfm", "name": "Large Scale Facial Model", "maker": "imperial", "family": "imperial/lsfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5905, "dossier_id": "imperial/retinaface/retinaface-r50", "name": "RetinaFace ResNet-50", "maker": "imperial", "family": "imperial/retinaface", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5906, "dossier_id": "imperial/scrfd/scrfd-10g", "name": "SCRFD-10G", "maker": "imperial", "family": "imperial/scrfd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5907, "dossier_id": "imperial/scrfd/scrfd-1g", "name": "SCRFD-1G", "maker": "imperial", "family": "imperial/scrfd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5908, "dossier_id": "imperial/scrfd/scrfd-2-5g", "name": "SCRFD-2.5G", "maker": "imperial", "family": "imperial/scrfd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5909, "dossier_id": "imperial/scrfd/scrfd-34g", "name": "SCRFD-34G", "maker": "imperial", "family": "imperial/scrfd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5910, "dossier_id": "imperial/scrfd/scrfd-500m", "name": "SCRFD-500M", "maker": "imperial", "family": "imperial/scrfd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5911, "dossier_id": "imperial/sononet/sononet-16", "name": "SonoNet-16", "maker": "imperial", "family": "imperial/sononet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5912, "dossier_id": "imperial/sononet/sononet-32", "name": "SonoNet-32", "maker": "imperial", "family": "imperial/sononet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5913, "dossier_id": "imperial/sononet/sononet-64", "name": "SonoNet-64", "maker": "imperial", "family": "imperial/sononet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5914, "dossier_id": "inception/mercury/mercury", "name": "Mercury", "maker": "inception", "family": "inception/mercury", "context_length": null, "prompt_price_per_m": 0.25, "completion_price_per_m": 1.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5915, "dossier_id": "inception/mercury/mercury-decide", "name": "Mercury Decide", "maker": "inception", "family": "inception/mercury", "context_length": null, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5916, "dossier_id": "inception/mercury/mercury-edit", "name": "Mercury Edit", "maker": "inception", "family": "inception/mercury", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5917, "dossier_id": "inception/mercury/mercury-edit-2", "name": "Mercury Edit 2", "maker": "inception", "family": "inception/mercury", "context_length": null, "prompt_price_per_m": 0.25, "completion_price_per_m": 0.75, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5918, "dossier_id": "inception/mercury/mercury-router", "name": "Mercury Router", "maker": "inception", "family": "inception/mercury", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5919, "dossier_id": "indiana/botometer/botometer", "name": "Botometer", "maker": "indiana", "family": "indiana/botometer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5920, "dossier_id": "indiana/botometer/botometer-lite", "name": "BotometerLite", "maker": "indiana", "family": "indiana/botometer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5921, "dossier_id": "inflection/inflection/inflection-1", "name": "Inflection-1", "maker": "inflection", "family": "inflection/inflection", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5922, "dossier_id": "inflection/inflection/inflection-2", "name": "Inflection-2", "maker": "inflection", "family": "inflection/inflection", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5923, "dossier_id": "inflection/inflection/inflection-2-5", "name": "Inflection-2.5", "maker": "inflection", "family": "inflection/inflection", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5924, "dossier_id": "inflection/pi/pi-3-1", "name": "Pi 3.1 Preview", "maker": "inflection", "family": "inflection/pi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5925, "dossier_id": "inria/camembert-bio/camembert-bio-gliner-v0-1", "name": "CamemBERT-bio-gliner v0.1", "maker": "inria", "family": "inria/camembert-bio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5926, "dossier_id": "insta360/canvas360/canvas360", "name": "Canvas360", "maker": "insta360", "family": "insta360/canvas360", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5927, "dossier_id": "insta360/dap/dap", "name": "Depth Any Panoramas", "maker": "insta360", "family": "insta360/dap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5928, "dossier_id": "insta360/dit360/dit360", "name": "DiT360", "maker": "insta360", "family": "insta360/dit360", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5929, "dossier_id": "insta360/panomind/panomind", "name": "Insta360 PanoMind", "maker": "insta360", "family": "insta360/panomind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5930, "dossier_id": "insta360/panoworld/panoworld", "name": "PanoWorld", "maker": "insta360", "family": "insta360/panoworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5931, "dossier_id": "insta360/unisharp/unisharp", "name": "UniSHARP", "maker": "insta360", "family": "insta360/unisharp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5932, "dossier_id": "inworld/stt/inworld-stt-1", "name": "Realtime STT-1", "maker": "inworld", "family": "inworld/stt", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Inworld Realtime STT-1 (`inworld/inworld-stt-1`) is a first-party speech model. It is active as of 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5933, "dossier_id": "inworld/tts/inworld-tts-1", "name": "Inworld TTS-1", "maker": "inworld", "family": "inworld/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Inworld Inworld TTS-1 (`inworld-tts-1`) is a first-party speech model. It is retired as of 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5934, "dossier_id": "inworld/tts/inworld-tts-1-5-max", "name": "Inworld TTS-1.5 Max", "maker": "inworld", "family": "inworld/tts", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5935, "dossier_id": "inworld/tts/inworld-tts-1-5-mini", "name": "Inworld TTS-1.5 Mini", "maker": "inworld", "family": "inworld/tts", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The latency-first 1.5 model, for realtime agents where time-to-first-audio outranked the Max checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5936, "dossier_id": "inworld/tts/inworld-tts-1-max", "name": "Inworld TTS-1 Max", "maker": "inworld", "family": "inworld/tts", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5937, "dossier_id": "inworld/tts/inworld-tts-2", "name": "Realtime TTS-2", "maker": "inworld", "family": "inworld/tts", "context_length": null, "prompt_price_per_m": 25.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Inworld Realtime TTS-2 (`inworld-tts-2`) is a first-party speech model. It is active as of 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5938, "dossier_id": "inworld/tts/inworld-tts-2-flash", "name": "Realtime TTS-2 Flash", "maker": "inworld", "family": "inworld/tts", "context_length": null, "prompt_price_per_m": 15.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5939, "dossier_id": "isi/embeddings/bengali-word-vectors", "name": "Bengali Word Vectors", "maker": "isi", "family": "isi/embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5940, "dossier_id": "isi/parser/bengali-dependency-parser", "name": "Bengali Dependency Parser", "maker": "isi", "family": "isi/parser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5941, "dossier_id": "isi/pos/bengali-pos-tagger", "name": "Bengali POS Tagger", "maker": "isi", "family": "isi/pos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5942, "dossier_id": "isi/transliteration/e2b-transliteration", "name": "E2B Transliteration", "maker": "isi", "family": "isi/transliteration", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5943, "dossier_id": "jadavpur/rectinet/rectinet", "name": "RectiNet", "maker": "jadavpur", "family": "jadavpur/rectinet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5944, "dossier_id": "jagiellonian/gepar3d/gepar3d", "name": "GEPAR3D", "maker": "jagiellonian", "family": "jagiellonian/gepar3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5945, "dossier_id": "jagiellonian/mat/mat-masking-200k", "name": "MAT masking 200k", "maker": "jagiellonian", "family": "jagiellonian/mat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5946, "dossier_id": "jagiellonian/mat/mat-masking-20m", "name": "MAT masking 20M", "maker": "jagiellonian", "family": "jagiellonian/mat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5947, "dossier_id": "jagiellonian/mat/mat-masking-2m", "name": "MAT masking 2M", "maker": "jagiellonian", "family": "jagiellonian/mat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5948, "dossier_id": "jagiellonian/rmat/rmat-4m", "name": "R-MAT 4M", "maker": "jagiellonian", "family": "jagiellonian/rmat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5949, "dossier_id": "jena/chemfuse/chemfuse", "name": "ChemFuse", "maker": "jena", "family": "jena/chemfuse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5950, "dossier_id": "jhu/bibert/bibert-ende", "name": "BiBERT English-German", "maker": "jhu", "family": "jhu/bibert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5951, "dossier_id": "jhu/kreyol-mt/kreyol-mt-scratch", "name": "Kreyol-MT from scratch", "maker": "jhu", "family": "jhu/kreyol-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5952, "dossier_id": "jhu/kreyol-mt/kreyol-mt-scratch-pubtrain", "name": "Kreyol-MT from scratch, public data", "maker": "jhu", "family": "jhu/kreyol-mt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5953, "dossier_id": "jhu/microbert/microbert", "name": "MicroBERT", "maker": "jhu", "family": "jhu/microbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5954, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5955, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5956, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5957, "dossier_id": "jhu/plaidx/plaidx-large-eng-tdist-mt5xxl-engeng", "name": "PLAID-X plaidx-large-eng-tdist-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5958, "dossier_id": "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-engeng", "name": "PLAID-X plaidx-large-fas-tdist-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5959, "dossier_id": "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-engfas", "name": "PLAID-X plaidx-large-fas-tdist-mt5xxl-engfas", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5960, "dossier_id": "jhu/plaidx/plaidx-large-fas-tdist-mt5xxl-fasfas", "name": "PLAID-X plaidx-large-fas-tdist-mt5xxl-fasfas", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5961, "dossier_id": "jhu/plaidx/plaidx-large-fas-tdist-t53b-engeng", "name": "PLAID-X plaidx-large-fas-tdist-t53b-engeng", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5962, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5963, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5964, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5965, "dossier_id": "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-engeng", "name": "PLAID-X plaidx-large-rus-tdist-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5966, "dossier_id": "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-engrus", "name": "PLAID-X plaidx-large-rus-tdist-mt5xxl-engrus", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5967, "dossier_id": "jhu/plaidx/plaidx-large-rus-tdist-mt5xxl-rusrus", "name": "PLAID-X plaidx-large-rus-tdist-mt5xxl-rusrus", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5968, "dossier_id": "jhu/plaidx/plaidx-large-rus-tdist-t53b-engeng", "name": "PLAID-X plaidx-large-rus-tdist-t53b-engeng", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5969, "dossier_id": "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-engeng", "name": "PLAID-X plaidx-large-zho-tdist-mt5xxl-engeng", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5970, "dossier_id": "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-engzho", "name": "PLAID-X plaidx-large-zho-tdist-mt5xxl-engzho", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5971, "dossier_id": "jhu/plaidx/plaidx-large-zho-tdist-mt5xxl-zhozho", "name": "PLAID-X plaidx-large-zho-tdist-mt5xxl-zhozho", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5972, "dossier_id": "jhu/plaidx/plaidx-large-zho-tdist-t53b-engeng", "name": "PLAID-X plaidx-large-zho-tdist-t53b-engeng", "maker": "jhu", "family": "jhu/plaidx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5973, "dossier_id": "jhu/rank-k/rank-k-32b", "name": "Rank-K-32B", "maker": "jhu", "family": "jhu/rank-k", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5974, "dossier_id": "jhu/rank1/rank1-0-5b", "name": "Rank1-0.5B", "maker": "jhu", "family": "jhu/rank1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5975, "dossier_id": "jhu/rank1/rank1-1-5b", "name": "Rank1-1.5B", "maker": "jhu", "family": "jhu/rank1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5976, "dossier_id": "jhu/rank1/rank1-14b", "name": "Rank1-14B", "maker": "jhu", "family": "jhu/rank1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5977, "dossier_id": "jhu/rank1/rank1-32b", "name": "Rank1-32B", "maker": "jhu", "family": "jhu/rank1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5978, "dossier_id": "jhu/rank1/rank1-3b", "name": "Rank1-3B", "maker": "jhu", "family": "jhu/rank1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5979, "dossier_id": "jhu/rank1/rank1-7b", "name": "Rank1-7B", "maker": "jhu", "family": "jhu/rank1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5980, "dossier_id": "jhu/rank1/rank1-llama3-8b", "name": "Rank1-Llama3-8B", "maker": "jhu", "family": "jhu/rank1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5981, "dossier_id": "jhu/rank1/rank1-mistral-2501-24b", "name": "Rank1-Mistral-2501-24B", "maker": "jhu", "family": "jhu/rank1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5982, "dossier_id": "jhu/spatialreasoner/spatialreasoner", "name": "SpatialReasoner", "maker": "jhu", "family": "jhu/spatialreasoner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5983, "dossier_id": "jhu/spatialreasoner/spatialreasoner-sft", "name": "SpatialReasoner-SFT", "maker": "jhu", "family": "jhu/spatialreasoner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5984, "dossier_id": "jhu/spatialreasoner/spatialreasoner-zero", "name": "SpatialReasoner-Zero", "maker": "jhu", "family": "jhu/spatialreasoner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5985, "dossier_id": "jina/embeddings/jina-embedding-l-en-v1", "name": "jina-embedding-l-en-v1", "maker": "jina", "family": "jina/embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5986, "dossier_id": "jina/embeddings/jina-embedding-s-en-v1", "name": "jina-embedding-s-en-v1", "maker": "jina", "family": "jina/embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5987, "dossier_id": "jina/embeddings/jina-embedding-t-en-v1", "name": "jina-embedding-t-en-v1", "maker": "jina", "family": "jina/embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "First-generation English sentence embedding. Weights are public. Only the base size remains on the 2026 model index.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5988, "dossier_id": "jyvaskyla/findoctype/findoctype", "name": "FinDoctype", "maker": "jyvaskyla", "family": "jyvaskyla/findoctype", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5989, "dossier_id": "jyvaskyla/graphbnc/graphbnc", "name": "GraphBNC", "maker": "jyvaskyla", "family": "jyvaskyla/graphbnc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5990, "dossier_id": "kaiber/flipbook/flipbook", "name": "Flipbook", "maker": "kaiber", "family": "kaiber/flipbook", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5991, "dossier_id": "kaiber/lip-sync/image-lip-sync", "name": "Image Lip Sync", "maker": "kaiber", "family": "kaiber/lip-sync", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5992, "dossier_id": "kaiber/lip-sync/video-lip-sync", "name": "Video Lip Sync", "maker": "kaiber", "family": "kaiber/lip-sync", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5993, "dossier_id": "kaiber/motion/motion", "name": "Motion", "maker": "kaiber", "family": "kaiber/motion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5994, "dossier_id": "kaiber/transform/transform-1-0", "name": "Transform 1.0", "maker": "kaiber", "family": "kaiber/transform", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5995, "dossier_id": "kaiber/transform/transform-2-0", "name": "Transform 2.0", "maker": "kaiber", "family": "kaiber/transform", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5996, "dossier_id": "kaiber/transform/video-restyle-1", "name": "Video Restyle 1", "maker": "kaiber", "family": "kaiber/transform", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5997, "dossier_id": "kaiber/transform/video-restyle-2", "name": "Video Restyle 2", "maker": "kaiber", "family": "kaiber/transform", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5998, "dossier_id": "kaist/atlas/atlasfold", "name": "AtlasFold", "maker": "kaist", "family": "kaist/atlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-protein structure prediction from sequence, using the AtlasLM-3B representation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 5999, "dossier_id": "kaist/atlas/atlasfold-m", "name": "AtlasFold-M", "maker": "kaist", "family": "kaist/atlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Protein-complex structure prediction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6000, "dossier_id": "kaist/atlas/atlaslm-3b", "name": "AtlasLM-3B", "maker": "kaist", "family": "kaist/atlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sequence representations and the language-model backbone for AtlasFold and AtlasFold-M.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6001, "dossier_id": "kaist/atlas/atlaslm-600m", "name": "AtlasLM-600M", "maker": "kaist", "family": "kaist/atlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sequence representations for proteins. The card contrasts the MIT weights with the more restrictive ESM-C license.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6002, "dossier_id": "kaist/cot/cot-t5-11b", "name": "CoT-T5 11B", "maker": "kaist", "family": "kaist/cot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6003, "dossier_id": "kaist/cot/cot-t5-3b", "name": "CoT-T5 3B", "maker": "kaist", "family": "kaist/cot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6004, "dossier_id": "kaist/janus/janus-7b", "name": "Janus 7B", "maker": "kaist", "family": "kaist/janus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Supervised fine-tune of Mistral-7B-v0.2 on the Multifaceted Collection so one model follows many system messages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6005, "dossier_id": "kaist/janus/janus-dpo-7b", "name": "Janus-DPO 7B", "maker": "kaist", "family": "kaist/janus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "DPO variant of Janus, trained on Multifaceted-Collection-DPO.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6006, "dossier_id": "kaist/janus/janus-orpo-7b", "name": "Janus-ORPO 7B", "maker": "kaist", "family": "kaist/janus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6007, "dossier_id": "kaist/janus/janus-rm-7b", "name": "Janus-RM 7B", "maker": "kaist", "family": "kaist/janus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6008, "dossier_id": "kaist/k-fold/k-fold", "name": "K-Fold", "maker": "kaist", "family": "kaist/k-fold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6009, "dossier_id": "kaist/kormo/kormo-vl", "name": "KORMo-VL", "maker": "kaist", "family": "kaist/kormo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Korean and English image-text tasks, including document-grounded reasoning, according to the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6010, "dossier_id": "kaist/kormo/kormo-vl-diffusion", "name": "KORMo-VL-Diffusion", "maker": "kaist", "family": "kaist/kormo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation with a high share of Korean daily scenes, according to the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6011, "dossier_id": "kaist/langbridge/codellama-langbridge-15b", "name": "Code Llama LangBridge 15B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6012, "dossier_id": "kaist/langbridge/codellama-langbridge-20b", "name": "Code Llama LangBridge 20B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6013, "dossier_id": "kaist/langbridge/codellama-langbridge-9b", "name": "Code Llama LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6014, "dossier_id": "kaist/langbridge/llama2-langbridge-9b", "name": "Llama 2 LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6015, "dossier_id": "kaist/langbridge/llemma-langbridge-9b", "name": "Llemma LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6016, "dossier_id": "kaist/langbridge/metamath-langbridge-15b", "name": "MetaMath LangBridge 15B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6017, "dossier_id": "kaist/langbridge/metamath-langbridge-20b", "name": "MetaMath LangBridge 20B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6018, "dossier_id": "kaist/langbridge/metamath-langbridge-9b", "name": "MetaMath LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6019, "dossier_id": "kaist/langbridge/orca2-langbridge-15b", "name": "Orca 2 LangBridge 15B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6020, "dossier_id": "kaist/langbridge/orca2-langbridge-20b", "name": "Orca 2 LangBridge 20B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6021, "dossier_id": "kaist/langbridge/orca2-langbridge-9b", "name": "Orca 2 LangBridge 9B", "maker": "kaist", "family": "kaist/langbridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6022, "dossier_id": "kaist/orpo/mistral-orpo-alpha", "name": "Mistral-ORPO-alpha", "maker": "kaist", "family": "kaist/orpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reference model for the ORPO paper: preference optimization without a separate reference model, on Mistral-7B-v0.1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6023, "dossier_id": "kaist/orpo/mistral-orpo-beta", "name": "Mistral-ORPO-beta", "maker": "kaist", "family": "kaist/orpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reference model for the ORPO paper: preference optimization without a separate reference model, on Mistral-7B-v0.1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6024, "dossier_id": "kaist/orpo/mistral-orpo-capybara-7k", "name": "Mistral-ORPO-Capybara-7k", "maker": "kaist", "family": "kaist/orpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reference model for the ORPO paper: preference optimization without a separate reference model, on Mistral-7B-v0.1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6025, "dossier_id": "kaist/prometheus/prometheus-13b-v1", "name": "Prometheus 13B v1.0", "maker": "kaist", "family": "kaist/prometheus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fine-grained scoring of long-form text against a user rubric, plus use as an open reward model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6026, "dossier_id": "kaist/prometheus/prometheus-2-bgb-8x7b", "name": "Prometheus-2-BGB 8x7B", "maker": "kaist", "family": "kaist/prometheus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6027, "dossier_id": "kaist/prometheus/prometheus-7b-v1", "name": "Prometheus 7B v1.0", "maker": "kaist", "family": "kaist/prometheus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6028, "dossier_id": "kaist/prometheus/prometheus-7b-v2", "name": "Prometheus 2 7B", "maker": "kaist", "family": "kaist/prometheus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score or rank model outputs under a user-written rubric, in both direct assessment and pairwise formats.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6029, "dossier_id": "kaist/prometheus/prometheus-8x7b-v2", "name": "Prometheus 2 8x7B", "maker": "kaist", "family": "kaist/prometheus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same job as Prometheus 2 7B: custom-rubric direct scores and pairwise rankings, at Mixtral scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6030, "dossier_id": "kaist/prometheus/prometheus-vision-13b", "name": "Prometheus-Vision 13B", "maker": "kaist", "family": "kaist/prometheus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cheap substitute for GPT-4V when the judge must see an image and grade a response on a custom rubric.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6031, "dossier_id": "kaist/prometheus/prometheus-vision-7b", "name": "Prometheus-Vision 7B", "maker": "kaist", "family": "kaist/prometheus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cheap substitute for GPT-4V when the judge must see an image and grade a response on a custom rubric.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6032, "dossier_id": "kaist/volcano/volcano-13b", "name": "Volcano 13B", "maker": "kaist", "family": "kaist/volcano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6033, "dossier_id": "kaist/volcano/volcano-7b", "name": "Volcano 7B", "maker": "kaist", "family": "kaist/volcano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6034, "dossier_id": "kakao/align/align-b7-base", "name": "COYO-ALIGN B7 Base", "maker": "kakao", "family": "kakao/align", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6035, "dossier_id": "kakao/honeybee/honeybee-c-13b-m256", "name": "Honeybee-C-13B-M256", "maker": "kakao", "family": "kakao/honeybee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6036, "dossier_id": "kakao/honeybee/honeybee-c-13b-m576", "name": "Honeybee-C-13B-M576", "maker": "kakao", "family": "kakao/honeybee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6037, "dossier_id": "kakao/honeybee/honeybee-c-7b-m144", "name": "Honeybee-C-7B-M144", "maker": "kakao", "family": "kakao/honeybee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6038, "dossier_id": "kakao/honeybee/honeybee-c-7b-m256", "name": "Honeybee-C-7B-M256", "maker": "kakao", "family": "kakao/honeybee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6039, "dossier_id": "kakao/honeybee/honeybee-d-13b-m256", "name": "Honeybee-D-13B-M256", "maker": "kakao", "family": "kakao/honeybee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6040, "dossier_id": "kakao/honeybee/honeybee-d-7b-m144", "name": "Honeybee-D-7B-M144", "maker": "kakao", "family": "kakao/honeybee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-and-text instruction following. The project compares C-Abstractor and D-Abstractor projectors at 7B and 13B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6041, "dossier_id": "kakao/kanana/kanana-2-0-9b", "name": "Kanana-2 0.9B", "maker": "kakao", "family": "kakao/kanana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6042, "dossier_id": "kakao/kanana/kanana-26-8b", "name": "Kanana 26.8B", "maker": "kakao", "family": "kakao/kanana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper treats it as the large staged pretrain, not as a separate product API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6043, "dossier_id": "kakao/kanana/kanana-8b-fc", "name": "Kanana 8B FC", "maker": "kakao", "family": "kakao/kanana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper says it is for Korean tool use after domain continued training and Korean function-calling fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6044, "dossier_id": "kakao/kanana/kanana-essence-9-8b", "name": "Kanana Essence 9.8B", "maker": "kakao", "family": "kakao/kanana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The report presents Essence as the 9.8B bilingual model between Nano and Flag.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6045, "dossier_id": "kakao/kanana/kanana-essence-9-8b-rag", "name": "Kanana Essence 9.8B RAG", "maker": "kakao", "family": "kakao/kanana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Kakao describes it as an Essence model trained to stay grounded in retrieved context while keeping instruct-model helpfulness.", "summary_intro": "Kanana Essence 9.8B RAG is the closed retrieval-augmented checkpoint in the Kanana technical report. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6046, "dossier_id": "kakao/kanana/kanana-flag-32-5b", "name": "Kanana Flag 32.5B", "maker": "kakao", "family": "kakao/kanana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6047, "dossier_id": "kakao/kanana/kanana-nano-2-1b-embedding", "name": "Kanana Nano 2.1B Embedding", "maker": "kakao", "family": "kakao/kanana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The technical report presents it as a retrieval backbone for English and Korean semantic search, not as a chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6048, "dossier_id": "kakao/kanana-a/kanana-a", "name": "Kanana-a", "maker": "kakao", "family": "kakao/kanana-a", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6049, "dossier_id": "kakao/kanana-safeguard/kanana-safeguard-siren-8b", "name": "Kanana Safeguard-Siren 8B", "maker": "kakao", "family": "kakao/kanana-safeguard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6050, "dossier_id": "kakao/karlo/karlo-v1-alpha", "name": "Karlo v1.0 alpha", "maker": "kakao", "family": "kakao/karlo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6051, "dossier_id": "kakao/lmspt/lmspt", "name": "LM-SPT 12.5 Hz v1", "maker": "kakao", "family": "kakao/lmspt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it turns speech into semantic and acoustic tokens that line up with language models, then reconstructs audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6052, "dossier_id": "kakao/mindall-e/mindall-e-1-3b", "name": "minDALL-E 1.3B", "maker": "kakao", "family": "kakao/mindall-e", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6053, "dossier_id": "kakao/vit/vit-l-16-coyo-224", "name": "ViT-L/16 COYO 224", "maker": "kakao", "family": "kakao/vit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Upstream image encoder and classification pretrain. Downstream ImageNet finetunes are separate dossiers.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6054, "dossier_id": "kakao/vit/vit-l-16-imagenet-384", "name": "ViT-L/16 ImageNet 384", "maker": "kakao", "family": "kakao/vit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6055, "dossier_id": "kakao/vit/vit-l-16-imagenet-512", "name": "ViT-L/16 ImageNet 512", "maker": "kakao", "family": "kakao/vit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification at 512. It is the stronger of the two public ImageNet finetunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6056, "dossier_id": "kansas/tinylidarnet/tinylidarnet", "name": "TinyLidarNet", "maker": "kansas", "family": "kansas/tinylidarnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6057, "dossier_id": "kapwing/i1/kapwing-i1", "name": "Kapwing I1", "maker": "kapwing", "family": "kapwing/i1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6058, "dossier_id": "kaust/attseg/attseg-internvl2-5-4b", "name": "AttSeg (InternVL2.5 4B)", "maker": "kaust", "family": "kaust/attseg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6059, "dossier_id": "kaust/attseg/attseg-internvl3-14b", "name": "AttSeg (InternVL3 14B)", "maker": "kaust", "family": "kaust/attseg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same referring-segmentation method as the 4B AttSeg folder, at the 14B size reported in the abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6060, "dossier_id": "kaust/bfpo/bfpo-mistral-7b", "name": "BFPO (Mistral 7B v0.1)", "maker": "kaust", "family": "kaust/bfpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6061, "dossier_id": "kaust/bfpo/bfpo-zephyr-7b-redteam", "name": "BFPO red-teaming (Zephyr 7B beta)", "maker": "kaust", "family": "kaust/bfpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6062, "dossier_id": "kaust/diffclip/diffclip-vit-b-16", "name": "DiffCLIP ViT-B/16", "maker": "kaust", "family": "kaust/diffclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6063, "dossier_id": "kaust/gsfm/gsfm", "name": "GSFM", "maker": "kaust", "family": "kaust/gsfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6064, "dossier_id": "kaust/hala/hala-1-2b", "name": "Hala 1.2B", "maker": "kaust", "family": "kaust/hala", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents Hala as an Arabic-centric instruction model. The family table compares Arabic multiple-choice and trust benchmarks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6065, "dossier_id": "kaust/hala/hala-1-2b-en-ar-translator", "name": "Hala 1.2B EN-AR Translator", "maker": "kaust", "family": "kaust/hala", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6066, "dossier_id": "kaust/hala/hala-350m", "name": "Hala 350M", "maker": "kaust", "family": "kaust/hala", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents Hala as an Arabic-centric instruction model. The family table compares Arabic multiple-choice and trust benchmarks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6067, "dossier_id": "kaust/hala/hala-700m", "name": "Hala 700M", "maker": "kaust", "family": "kaust/hala", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents Hala as an Arabic-centric instruction model. The family table compares Arabic multiple-choice and trust benchmarks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6068, "dossier_id": "kaust/hala/hala-9b", "name": "Hala 9B", "maker": "kaust", "family": "kaust/hala", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents Hala as an Arabic-centric instruction model. The family table compares Arabic multiple-choice and trust benchmarks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6069, "dossier_id": "kaust/imotion-llm/imotion-llm", "name": "iMotion-LLM", "maker": "kaust", "family": "kaust/imotion-llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The abstract says it generates feasible, safety-aligned driving trajectories from text instructions, for simulation, interpretability, and safety-alignment tests.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6070, "dossier_id": "kaust/longvu/longvu-llama3-2-1b", "name": "LongVU (Llama 3.2 1B)", "maker": "kaust", "family": "kaust/longvu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6071, "dossier_id": "kaust/longvu/longvu-llama3-2-3b", "name": "LongVU (Llama 3.2 3B)", "maker": "kaust", "family": "kaust/longvu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same long-video understanding job as the Qwen2 LongVU release, with a Llama-3.2 3B language backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6072, "dossier_id": "kaust/longvu/longvu-qwen2-7b", "name": "LongVU (Qwen2 7B)", "maker": "kaust", "family": "kaust/longvu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6073, "dossier_id": "kaust/mextract/mextract-0-5b", "name": "MeXtract 0.5B", "maker": "kaust", "family": "kaust/mextract", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6074, "dossier_id": "kaust/mextract/mextract-1-5b", "name": "MeXtract 1.5B", "maker": "kaust", "family": "kaust/mextract", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Schema-based metadata extraction, same task as MeXtract 0.5B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6075, "dossier_id": "kaust/mextract/mextract-3b", "name": "MeXtract 3B", "maker": "kaust", "family": "kaust/mextract", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Schema-based metadata extraction. On the shared MOLE+ table this is the highest MeXtract average.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6076, "dossier_id": "kaust/minigpt/minigpt-4-llama2-7b", "name": "MiniGPT-4 (Llama-2 Chat 7B)", "maker": "kaust", "family": "kaust/minigpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6077, "dossier_id": "kaust/minigpt/minigpt-4-vicuna-13b", "name": "MiniGPT-4 (Vicuna 13B)", "maker": "kaust", "family": "kaust/minigpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6078, "dossier_id": "kaust/minigpt/minigpt-4-vicuna-7b", "name": "MiniGPT-4 (Vicuna 7B)", "maker": "kaust", "family": "kaust/minigpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6079, "dossier_id": "kaust/minigpt/minigpt-med", "name": "MiniGPT-Med", "maker": "kaust", "family": "kaust/minigpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6080, "dossier_id": "kaust/minigpt/minigpt-v2", "name": "MiniGPT-v2", "maker": "kaust", "family": "kaust/minigpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6081, "dossier_id": "kaust/minigpt4-video/minigpt4-video", "name": "MiniGPT4-Video", "maker": "kaust", "family": "kaust/minigpt4-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6082, "dossier_id": "kaust/salle/salle-v1-5-1", "name": "SALL-E v1.5.1", "maker": "kaust", "family": "kaust/salle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6083, "dossier_id": "kaust/synthclip/synthclip-vit-b-16", "name": "SynthCLIP ViT-B/16", "maker": "kaust", "family": "kaust/synthclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6084, "dossier_id": "kaust/tempo/tempo-6b", "name": "Tempo-6B", "maker": "kaust", "family": "kaust/tempo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6085, "dossier_id": "kaust/videoauto-r1/videoauto-r1-qwen2-5-vl-7b", "name": "VideoAuto-R1 (Qwen2.5-VL 7B)", "maker": "kaust", "family": "kaust/videoauto-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6086, "dossier_id": "kaust/videoauto-r1/videoauto-r1-qwen3-vl-8b", "name": "VideoAuto-R1 (Qwen3-VL 8B)", "maker": "kaust", "family": "kaust/videoauto-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same reason-when-necessary video understanding job as the Qwen2.5-VL 7B checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6087, "dossier_id": "kcl/foresight/foresight", "name": "Foresight", "maker": "kcl", "family": "kcl/foresight", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6088, "dossier_id": "kcl/foresight/foresight-england", "name": "Foresight-England", "maker": "kcl", "family": "kcl/foresight", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6089, "dossier_id": "kcl/medcat/medcat-snomed-international", "name": "MedCAT SNOMED International", "maker": "kcl", "family": "kcl/medcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6090, "dossier_id": "kcl/medcat/medcat-snomed-uk", "name": "MedCAT SNOMED CT UK", "maker": "kcl", "family": "kcl/medcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6091, "dossier_id": "kcl/medcat/medcat-umls-full", "name": "MedCAT UMLS Full", "maker": "kcl", "family": "kcl/medcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6092, "dossier_id": "kcl/medcat/medcat-umls-small", "name": "MedCAT UMLS Small", "maker": "kcl", "family": "kcl/medcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6093, "dossier_id": "kcl/niftynet/anisotropic-nets-brats", "name": "Anisotropic nets BraTS", "maker": "kcl", "family": "kcl/niftynet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6094, "dossier_id": "kcl/niftynet/dense-vnet-abdominal-ct", "name": "Dense V-Net abdominal CT", "maker": "kcl", "family": "kcl/niftynet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6095, "dossier_id": "kcl/niftynet/highres3dnet-brain-parcellation", "name": "HighRes3DNet brain parcellation", "maker": "kcl", "family": "kcl/niftynet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6096, "dossier_id": "kcl/niftynet/ultrasound-simulator-gan", "name": "Ultrasound simulator GAN", "maker": "kcl", "family": "kcl/niftynet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6097, "dossier_id": "kddi/bousai/bousai-vlm", "name": "\u9632\u707dVLM", "maker": "kddi", "family": "kddi/bousai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6098, "dossier_id": "kddi/ks-mgsr/ks-mgsr", "name": "KS-MGSR", "maker": "kddi", "family": "kddi/ks-mgsr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6099, "dossier_id": "keio/polos/polos", "name": "Polos", "maker": "keio", "family": "keio/polos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6100, "dossier_id": "kentucky/dale-ct/dale-ct-0", "name": "DALE-CT-0", "maker": "kentucky", "family": "kentucky/dale-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6101, "dossier_id": "kentucky/dale-ct/dale-ct-0-l", "name": "DALE-CT-0-L", "maker": "kentucky", "family": "kentucky/dale-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6102, "dossier_id": "kentucky/dale-ct/dale-ct-1s", "name": "DALE-CT-1S", "maker": "kentucky", "family": "kentucky/dale-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6103, "dossier_id": "kentucky/dale-ct/dale-ct-1s-v2", "name": "DALE-CT-1S-v2", "maker": "kentucky", "family": "kentucky/dale-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research chest CT backbone. The card says it isolates anatomical supervision against DALE-CT-2S.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6104, "dossier_id": "kentucky/dale-ct/dale-ct-2s", "name": "DALE-CT-2S", "maker": "kentucky", "family": "kentucky/dale-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research chest CT feature extractor. The card says dual-source dense supervision tracks both anatomy and abnormalities.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6105, "dossier_id": "kentucky/dale-ct/finetuned-dinov2-chest-ct", "name": "Finetuned DINOv2 Chest CT", "maker": "kentucky", "family": "kentucky/dale-ct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6106, "dossier_id": "kentucky/ker/ker-vljepa-3b", "name": "Ker-VLJEPA-3B", "maker": "kentucky", "family": "kentucky/ker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6107, "dossier_id": "kentucky/kos/kos-v5-retriever", "name": "KOS-V5-Retriever", "maker": "kentucky", "family": "kentucky/kos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6108, "dossier_id": "kentucky/melt/melt-llama-2-3x70b-chat", "name": "MELT-Llama-2-3x70B-Chat", "maker": "kentucky", "family": "kentucky/melt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6109, "dossier_id": "kentucky/melt/melt-llama-2-7b-chat-v0-1", "name": "MELT-Llama-2-7B-Chat-v0.1", "maker": "kentucky", "family": "kentucky/melt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6110, "dossier_id": "kentucky/melt/melt-mistral-3x7b-instruct-v0-1", "name": "MELT-Mistral-3x7B-Instruct-v0.1", "maker": "kentucky", "family": "kentucky/melt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6111, "dossier_id": "kentucky/melt/melt-mixtral-8x7b-instruct-v0-1", "name": "MELT-Mixtral-8x7B-Instruct-v0.1", "maker": "kentucky", "family": "kentucky/melt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6112, "dossier_id": "kentucky/melt/melt-tinyllama-1-1b-chat-v1-0", "name": "MELT-TinyLlama-1.1B-Chat-v1.0", "maker": "kentucky", "family": "kentucky/melt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6113, "dossier_id": "kentucky/np/mad-np", "name": "MAD-NP", "maker": "kentucky", "family": "kentucky/np", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6114, "dossier_id": "kentucky/np/np-giant", "name": "NP-GIANT", "maker": "kentucky", "family": "kentucky/np", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6115, "dossier_id": "kentucky/np/np-test-0", "name": "NP-TEST-0", "maker": "kentucky", "family": "kentucky/np", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6116, "dossier_id": "kfupm/msrnet/msrnet", "name": "MSRNet", "maker": "kfupm", "family": "kfupm/msrnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6117, "dossier_id": "kfupm/xlsr-ar/xlsr-ar", "name": "XLSR-AR", "maker": "kfupm", "family": "kfupm/xlsr-ar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6118, "dossier_id": "kit/beast/beast", "name": "BEAST", "maker": "kit", "family": "kit/beast", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6119, "dossier_id": "kit/beast/beast-f", "name": "BEAST-F", "maker": "kit", "family": "kit/beast", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6120, "dossier_id": "kit/beso/beso", "name": "BESO", "maker": "kit", "family": "kit/beso", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6121, "dossier_id": "kit/flower/flower", "name": "FLOWER", "maker": "kit", "family": "kit/flower", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6122, "dossier_id": "kit/mdt/mdt", "name": "MDT", "maker": "kit", "family": "kit/mdt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6123, "dossier_id": "kit/mdt/mdt-v", "name": "MDT-V", "maker": "kit", "family": "kit/mdt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6124, "dossier_id": "kit/mode/mode", "name": "MoDE", "maker": "kit", "family": "kit/mode", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6125, "dossier_id": "konstanz/animal2vec/animal2vec", "name": "animal2vec", "maker": "konstanz", "family": "konstanz/animal2vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6126, "dossier_id": "konstanz/embryonet/embryonet", "name": "EmbryoNet", "maker": "konstanz", "family": "konstanz/embryonet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6127, "dossier_id": "korea-university/kullm/kullm-r", "name": "KULLM-R", "maker": "korea-university", "family": "korea-university/kullm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6128, "dossier_id": "korea-university/kure/kure-v2-unsupervised", "name": "KURE-v2-unsupervised", "maker": "korea-university", "family": "korea-university/kure", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6129, "dossier_id": "krea/krea-1/flux-1-krea", "name": "FLUX.1 Krea [dev]", "maker": "krea", "family": "krea/krea-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6130, "dossier_id": "krea/krea-1/krea-1", "name": "Krea 1", "maker": "krea", "family": "krea/krea-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6131, "dossier_id": "krea/krea-2/krea-2-large", "name": "Krea 2 Large", "maker": "krea", "family": "krea/krea-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.06, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6132, "dossier_id": "krea/krea-2/krea-2-medium", "name": "Krea 2 Medium", "maker": "krea", "family": "krea/krea-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6133, "dossier_id": "krea/krea-2/krea-2-raw", "name": "Krea 2 Raw", "maker": "krea", "family": "krea/krea-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6134, "dossier_id": "krea/krea-2/krea-2-turbo", "name": "Krea 2 Turbo", "maker": "krea", "family": "krea/krea-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.015, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6135, "dossier_id": "krea/krea-3/krea-3", "name": "Krea 3", "maker": "krea", "family": "krea/krea-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.06, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6136, "dossier_id": "krea/krea-realtime/krea-realtime-14b", "name": "Krea Realtime 14B", "maker": "krea", "family": "krea/krea-realtime", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6137, "dossier_id": "kth/diff-ttsg/diff-ttsg", "name": "Diff-TTSG", "maker": "kth", "family": "kth/diff-ttsg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6138, "dossier_id": "kth/match-ttsg/match-ttsg", "name": "Match-TTSG", "maker": "kth", "family": "kth/match-ttsg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6139, "dossier_id": "kth/matcha/matcha-tts", "name": "Matcha-TTS", "maker": "kth", "family": "kth/matcha", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6140, "dossier_id": "kth/neural-hmm/neural-hmm", "name": "Neural HMM TTS", "maker": "kth", "family": "kth/neural-hmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6141, "dossier_id": "kth/overflow/overflow", "name": "OverFlow", "maker": "kth", "family": "kth/overflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6142, "dossier_id": "kth/voxtream/voxtream", "name": "VoXtream", "maker": "kth", "family": "kth/voxtream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6143, "dossier_id": "kth/voxtream/voxtream2", "name": "VoXtream2", "maker": "kth", "family": "kth/voxtream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6144, "dossier_id": "kuaishou/keye/keye-vl-1-5-8b", "name": "Kwai Keye-VL 1.5 8B", "maker": "kuaishou", "family": "kuaishou/keye", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It is for longer video understanding, image questions, and reasoning, with thinking and non-thinking prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6145, "dossier_id": "kuaishou/keye/keye-vl-2-0-30b-a3b", "name": "Keye-VL 2.0 30B-A3B", "maker": "kuaishou", "family": "kuaishou/keye", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6146, "dossier_id": "kuaishou/keye/keye-vl-671b-a37b", "name": "Keye-VL 671B-A37B", "maker": "kuaishou", "family": "kuaishou/keye", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6147, "dossier_id": "kuaishou/keye/keye-vl-8b-preview", "name": "Kwai Keye-VL 8B Preview", "maker": "kuaishou", "family": "kuaishou/keye", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6148, "dossier_id": "kuaishou/kling-image/kling-image-o1", "name": "Kling Image O1", "maker": "kuaishou", "family": "kuaishou/kling-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.028, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6149, "dossier_id": "kuaishou/kling-image/kling-image-v1", "name": "Kling Image 1.0", "maker": "kuaishou", "family": "kuaishou/kling-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It does text-to-image and whole-image image-to-image at 1K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6150, "dossier_id": "kuaishou/kling-image/kling-image-v1-5", "name": "Kling Image 1.5", "maker": "kuaishou", "family": "kuaishou/kling-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Use face reference to keep a face and change clothing or background, or subject reference to keep the whole character.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6151, "dossier_id": "kuaishou/kling-image/kling-image-v2", "name": "Kling Image 2.0", "maker": "kuaishou", "family": "kuaishou/kling-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Chinese price page lists text-to-image at 4 points and multi-image reference at 16 points.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6152, "dossier_id": "kuaishou/kling-image/kling-image-v2-1", "name": "Kling Image 2.1", "maker": "kuaishou", "family": "kuaishou/kling-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.014, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6153, "dossier_id": "kuaishou/kling-image/kling-image-v3", "name": "Kling Image 3.0", "maker": "kuaishou", "family": "kuaishou/kling-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.028, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6154, "dossier_id": "kuaishou/kling-image/kling-image-v3-omni", "name": "Kling Image 3.0 Omni", "maker": "kuaishou", "family": "kuaishou/kling-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.028, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Kuaishou describes cinematic narrative stills, group-image generation, and 2K or 4K direct output, with multi-image subject control.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6155, "dossier_id": "kuaishou/kling-image/kling-v2-new", "name": "Kling Image 2.0 New", "maker": "kuaishou", "family": "kuaishou/kling-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It restyles an existing image. It is not the general Kling Image 2.0 text-to-image model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6156, "dossier_id": "kuaishou/kling-video/kling-3-0-turbo", "name": "Kling 3.0 Turbo", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6157, "dossier_id": "kuaishou/kling-video/kling-4-0", "name": "Kling 4.0", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6158, "dossier_id": "kuaishou/kling-video/kling-4-0-flash", "name": "Kling 4.0 Flash", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6159, "dossier_id": "kuaishou/kling-video/kling-v1", "name": "Kling 1.0", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6160, "dossier_id": "kuaishou/kling-video/kling-v1-5", "name": "Kling 1.5", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6161, "dossier_id": "kuaishou/kling-video/kling-v1-6", "name": "Kling 1.6", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6162, "dossier_id": "kuaishou/kling-video/kling-v2-1", "name": "Kling 2.1", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6163, "dossier_id": "kuaishou/kling-video/kling-v2-1-master", "name": "Kling 2.1 Master", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The matrix shows 5-second and 10-second generation and no extra control plugins.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6164, "dossier_id": "kuaishou/kling-video/kling-v2-5-turbo", "name": "Kling 2.5 Turbo", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6165, "dossier_id": "kuaishou/kling-video/kling-v2-6", "name": "Kling 2.6", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6166, "dossier_id": "kuaishou/kling-video/kling-v2-master", "name": "Kling 2.0 Master", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6167, "dossier_id": "kuaishou/kling-video/kling-v3", "name": "Kling 3.0", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6168, "dossier_id": "kuaishou/kling-video/kling-v3-omni", "name": "Kling 3.0 Omni", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6169, "dossier_id": "kuaishou/kling-video/kling-video-o1", "name": "Kling O1", "maker": "kuaishou", "family": "kuaishou/kling-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6170, "dossier_id": "kuaishou/kolors/kolors-controlnet-canny", "name": "Kolors ControlNet Canny", "maker": "kuaishou", "family": "kuaishou/kolors", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6171, "dossier_id": "kuaishou/kolors/kolors-controlnet-depth", "name": "Kolors ControlNet Depth", "maker": "kuaishou", "family": "kuaishou/kolors", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6172, "dossier_id": "kuaishou/kolors/kolors-controlnet-pose", "name": "Kolors ControlNet Pose", "maker": "kuaishou", "family": "kuaishou/kolors", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It is for pose-guided image generation on the Kolors base. Usage steps were not on a card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6173, "dossier_id": "kuaishou/kolors/kolors-inpainting", "name": "Kolors Inpainting", "maker": "kuaishou", "family": "kuaishou/kolors", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It fills a masked region from a text prompt while leaving the rest of the image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6174, "dossier_id": "kuaishou/kolors/kolors-ip-adapter-faceid-plus", "name": "Kolors IP-Adapter FaceID Plus", "maker": "kuaishou", "family": "kuaishou/kolors", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6175, "dossier_id": "kuaishou/kolors/kolors-ip-adapter-plus", "name": "Kolors IP-Adapter Plus", "maker": "kuaishou", "family": "kuaishou/kolors", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6176, "dossier_id": "kuaishou/kolors/kolors-virtual-try-on", "name": "Kolors Virtual Try-On", "maker": "kuaishou", "family": "kuaishou/kolors", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6177, "dossier_id": "kuaishou/liveportrait/liveportrait", "name": "LivePortrait", "maker": "kuaishou", "family": "kuaishou/liveportrait", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6178, "dossier_id": "kyutai/arc-encoder/arc4-encoder-llama", "name": "ARC4 Encoder Llama", "maker": "kyutai", "family": "kyutai/arc-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Describes the three ARC8 encoders. Backbone is Llama 3.2 3B. Llama license also applies.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6179, "dossier_id": "kyutai/arc-encoder/arc8-encoder-llama", "name": "ARC8 Encoder Llama", "maker": "kyutai", "family": "kyutai/arc-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Describes the three ARC8 encoders. Backbone is Llama 3.2 3B. Llama license also applies.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6180, "dossier_id": "kyutai/arc-encoder/arc8-encoder-mistral", "name": "ARC8 Encoder Mistral", "maker": "kyutai", "family": "kyutai/arc-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Describes the three ARC8 encoders. Backbone is Llama 3.2 3B. Llama license also applies.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6181, "dossier_id": "kyutai/arc-encoder/arc8-encoder-multi", "name": "ARC8 Encoder multi", "maker": "kyutai", "family": "kyutai/arc-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Describes the three ARC8 encoders. Backbone is Llama 3.2 3B. Llama license also applies.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6182, "dossier_id": "kyutai/casa/casa-helium1-vl-2b", "name": "CASA-Helium1-VL-2B", "maker": "kyutai", "family": "kyutai/casa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scores and license. Qwen research license also applies.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6183, "dossier_id": "kyutai/casa/casa-qwen2-5-vl-3b", "name": "CASA-Qwen2.5-VL-3B", "maker": "kyutai", "family": "kyutai/casa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Defines this checkpoint as the frozen-backbone Qwen adaptation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6184, "dossier_id": "kyutai/casa/casa-qwen2-5-vl-3b-livecc", "name": "CASA-Qwen2.5-VL-3B LiveCC", "maker": "kyutai", "family": "kyutai/casa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hub id. Dataset tag chenjoya/Live-WhisperX-526K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6185, "dossier_id": "kyutai/dactory/dactory", "name": "Dactory classifiers", "maker": "kyutai", "family": "kyutai/dactory", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Usage via fasttext. One bin file per language, for example filter_en.bin.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6186, "dossier_id": "kyutai/helium/helium-6b", "name": "Helium 6B", "maker": "kyutai", "family": "kyutai/helium", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Documents sequential and shuffled subfolders, token counts, and OLMES scores.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6187, "dossier_id": "kyutai/helium/helium1-vl-2b", "name": "Helium1-VL 2B", "maker": "kyutai", "family": "kyutai/helium", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruct-tuned insertion VLM. Qwen research license also applies to the frozen encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6188, "dossier_id": "kyutai/hibiki/hibiki-1b", "name": "Hibiki-M", "maker": "kyutai", "family": "kyutai/hibiki", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Called Hibiki-M in the paper. 1.7B, 1.1 kbps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6189, "dossier_id": "kyutai/hibiki/hibiki-2b", "name": "Hibiki", "maker": "kyutai", "family": "kyutai/hibiki", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "French to English. Card says 2.7B. CC-BY.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6190, "dossier_id": "kyutai/hibiki/hibiki-zero", "name": "Hibiki-Zero", "maker": "kyutai", "family": "kyutai/hibiki", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "3B. CC-BY-NC-SA 4.0. Four source languages into English.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6191, "dossier_id": "kyutai/mimi/mimi", "name": "Mimi", "maker": "kyutai", "family": "kyutai/mimi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming speech codec used by Moshi, Hibiki, STT, and TTS.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6192, "dossier_id": "kyutai/mira/mira", "name": "MIRA", "maker": "kyutai", "family": "kyutai/mira", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lists MIRA as a playable real-time multiplayer world model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6193, "dossier_id": "kyutai/moshi/moshi-rag", "name": "MoshiRAG", "maker": "kyutai", "family": "kyutai/moshi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Front-end weights. Retrieval back end is pluggable and not in the checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6194, "dossier_id": "kyutai/moshi/moshi-vis", "name": "MoshiVis", "maker": "kyutai", "family": "kyutai/moshi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Female-voice vision-speech model. Gemma terms apply to the bundled vision encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6195, "dossier_id": "kyutai/moshi/moshika", "name": "Moshika", "maker": "kyutai", "family": "kyutai/moshi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Release notes for Moshiko, Moshika, and Mimi. Code is Apache-2.0; weights are CC-BY 4.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6196, "dossier_id": "kyutai/moshi/moshika-rl", "name": "Moshika RL (Seamless)", "maker": "kyutai", "family": "kyutai/moshi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The model card was gated on 2026-10-15, so this dossier uses the Hub metadata and the paper abstract.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6197, "dossier_id": "kyutai/moshi/moshiko", "name": "Moshiko", "maker": "kyutai", "family": "kyutai/moshi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Release notes for Moshiko, Moshika, and Mimi. Code is Apache-2.0; weights are CC-BY 4.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6198, "dossier_id": "kyutai/muscriptor/muscriptor-large", "name": "MuScriptor large", "maker": "kyutai", "family": "kyutai/muscriptor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Size table and a D_Real-only F1 comparison. Weights are gated behind CC BY-NC 4.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6199, "dossier_id": "kyutai/muscriptor/muscriptor-medium", "name": "MuScriptor medium", "maker": "kyutai", "family": "kyutai/muscriptor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Size table and a D_Real-only F1 comparison. Weights are gated behind CC BY-NC 4.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6200, "dossier_id": "kyutai/muscriptor/muscriptor-small", "name": "MuScriptor small", "maker": "kyutai", "family": "kyutai/muscriptor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Size table and a D_Real-only F1 comparison. Weights are gated behind CC BY-NC 4.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6201, "dossier_id": "kyutai/ovie/ovie", "name": "OVIE", "maker": "kyutai", "family": "kyutai/ovie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Architecture, the four checkpoints, and the metric table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6202, "dossier_id": "kyutai/ovie/ovie-512", "name": "OVIE-512", "maker": "kyutai", "family": "kyutai/ovie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Architecture, the four checkpoints, and the metric table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6203, "dossier_id": "kyutai/ovie/ovie-ft-dl3dv", "name": "OVIE-ft DL3DV", "maker": "kyutai", "family": "kyutai/ovie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Architecture, the four checkpoints, and the metric table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6204, "dossier_id": "kyutai/ovie/ovie-ft-re10k", "name": "OVIE-ft RealEstate10K", "maker": "kyutai", "family": "kyutai/ovie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Architecture, the four checkpoints, and the metric table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6205, "dossier_id": "kyutai/pocket-tts/pocket-tts", "name": "Pocket TTS", "maker": "kyutai", "family": "kyutai/pocket-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Install, CLI, latency, languages, and voice list. Code is MIT. Hub metadata tags the weights CC-BY 4.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6206, "dossier_id": "kyutai/pocket-tts/pocket-tts-without-voice-cloning", "name": "Pocket TTS without voice cloning", "maker": "kyutai", "family": "kyutai/pocket-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Gated on 2026-10-15. Metadata only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6207, "dossier_id": "kyutai/stt/stt-1b-en-fr", "name": "Kyutai STT 1B English-French", "maker": "kyutai", "family": "kyutai/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6208, "dossier_id": "kyutai/stt/stt-2-6b-en", "name": "Kyutai STT 2.6B English", "maker": "kyutai", "family": "kyutai/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming English ASR. CC-BY 4.0. Transformers port is stt-2.6b-en-trfs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6209, "dossier_id": "kyutai/surflo/surflo", "name": "Surflo", "maker": "kyutai", "family": "kyutai/surflo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Points at AntoineGuedon/Surflo-v0, file surflo_v0.pt.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6210, "dossier_id": "kyutai/tts/tts-0-75b-en-public", "name": "Kyutai TTS 0.75B public", "maker": "kyutai", "family": "kyutai/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scientific-comparison checkpoint. CC-BY 4.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6211, "dossier_id": "kyutai/tts/tts-1-6b-en-fr", "name": "Kyutai TTS 1.6B", "maker": "kyutai", "family": "kyutai/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English and French streaming TTS. CC-BY 4.0. Voices live in kyutai/tts-voices.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6212, "dossier_id": "kyutai/voice-of-reason/glm-4-voice-of-reason-9b", "name": "GLM-4 Voice of Reason", "maker": "kyutai", "family": "kyutai/voice-of-reason", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Run instructions and the 0.706 GSM8K figure. License inherited from GLM-4-Voice.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6213, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hub id. Base zai-org/glm-4-voice-9b. License other.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6214, "dossier_id": "lanzhou/qiwu/qiwu-zhisuan", "name": "\u9f50\u7269\u667a\u7b97", "maker": "lanzhou", "family": "lanzhou/qiwu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6215, "dossier_id": "leeds/nsp/nsp", "name": "Neural Social Physics (NSP)", "maker": "leeds", "family": "leeds/nsp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6216, "dossier_id": "lehigh/biomedgpt/biomedgpt", "name": "BiomedGPT", "maker": "lehigh", "family": "lehigh/biomedgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6217, "dossier_id": "lehigh/braingfm/braingfm", "name": "BrainGFM", "maker": "lehigh", "family": "lehigh/braingfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6218, "dossier_id": "leipzig/hypermpnn/hypermpnn", "name": "HyperMPNN", "maker": "leipzig", "family": "leipzig/hypermpnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6219, "dossier_id": "lenovo/quality-inspection/industrial-quality-inspection", "name": "Lenovo Industrial Quality Inspection Foundation Model", "maker": "lenovo", "family": "lenovo/quality-inspection", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6220, "dossier_id": "lenovo/tianxi/tianxi-memory-1-7b", "name": "\u5929\u79a7\u591a\u6a21\u6001\u8bb0\u5fc6\u6a21\u578b", "maker": "lenovo", "family": "lenovo/tianxi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6221, "dossier_id": "leonardo/anime/anime-xl", "name": "Leonardo Anime XL", "maker": "leonardo", "family": "leonardo/anime", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6222, "dossier_id": "leonardo/creative/leonardo-creative", "name": "Leonardo Creative", "maker": "leonardo", "family": "leonardo/creative", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6223, "dossier_id": "leonardo/diffusion/diffusion-xl", "name": "Leonardo Diffusion XL", "maker": "leonardo", "family": "leonardo/diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6224, "dossier_id": "leonardo/kino/kino-xl", "name": "Leonardo Kino XL", "maker": "leonardo", "family": "leonardo/kino", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6225, "dossier_id": "leonardo/lightning/lightning-xl", "name": "Leonardo Lightning XL", "maker": "leonardo", "family": "leonardo/lightning", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6226, "dossier_id": "leonardo/lucid/lucid-origin", "name": "Lucid Origin", "maker": "leonardo", "family": "leonardo/lucid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6227, "dossier_id": "leonardo/lucid/lucid-realism", "name": "Lucid Realism", "maker": "leonardo", "family": "leonardo/lucid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6228, "dossier_id": "leonardo/motion/motion-2-0", "name": "Motion 2.0", "maker": "leonardo", "family": "leonardo/motion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6229, "dossier_id": "leonardo/motion/motion-2-0-fast", "name": "Motion 2.0 Fast", "maker": "leonardo", "family": "leonardo/motion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6230, "dossier_id": "leonardo/phoenix/phoenix-0-9", "name": "Phoenix 0.9", "maker": "leonardo", "family": "leonardo/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6231, "dossier_id": "leonardo/phoenix/phoenix-1-0", "name": "Phoenix 1.0", "maker": "leonardo", "family": "leonardo/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6232, "dossier_id": "leonardo/select/leonardo-select", "name": "Leonardo Select", "maker": "leonardo", "family": "leonardo/select", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The only job statement found is the platform description: a powerful SD 2.1 fine-tune for realism.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6233, "dossier_id": "leonardo/signature/leonardo-signature", "name": "Leonardo Signature", "maker": "leonardo", "family": "leonardo/signature", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6234, "dossier_id": "leonardo/vision/vision-xl", "name": "Leonardo Vision XL", "maker": "leonardo", "family": "leonardo/vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6235, "dossier_id": "leuven/nelf/asr-subtitles-v1", "name": "NeLF ASR Subtitles v1", "maker": "leuven", "family": "leuven/nelf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flemish Dutch speech to both a verbatim transcript and a subtitle transcript, from separate decoders.", "summary_intro": "KU Leuven model NeLF ASR Subtitles v1 (asr-subtitles-v1). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6236, "dossier_id": "leuven/nelf/asr-subtitles-v2", "name": "NeLF ASR Subtitles v2", "maker": "leuven", "family": "leuven/nelf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flemish Dutch speech to a verbatim transcript with annotation tags and, through a second decoder, a cleaned subtitle transcript.", "summary_intro": "KU Leuven model NeLF ASR Subtitles v2 (asr-subtitles-v2). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6237, "dossier_id": "leuven/nelf/asr-subtitles-v2-small", "name": "NeLF ASR Subtitles v2 small", "maker": "leuven", "family": "leuven/nelf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The same verbatim-plus-subtitle job as ASR Subtitles v2, at the 70M size named on the org leaderboard.", "summary_intro": "KU Leuven model NeLF ASR Subtitles v2 small (asr-subtitles-v2-small). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6238, "dossier_id": "leuven/nelf/asr-verbatim-v1", "name": "NeLF ASR Verbatim v1", "maker": "leuven", "family": "leuven/nelf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flemish Dutch speech to a verbatim transcript only. Subtitle decoding stays on the dual-decoder v1 checkpoint.", "summary_intro": "KU Leuven model NeLF ASR Verbatim v1 (asr-verbatim-v1). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6239, "dossier_id": "leuven/nelf/nelf-s2t-pytorch", "name": "NeLF S2T PyTorch", "maker": "leuven", "family": "leuven/nelf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Joint verbatim transcription, including annotation tags, and cleaned subtitle text for Flemish Dutch. Two decoders emit the two forms.", "summary_intro": "KU Leuven model NeLF S2T PyTorch (nelf-s2t-pytorch). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6240, "dossier_id": "leuven/robbertje/robbertje-1-gb-non-shuffled", "name": "RobBERTje 1GB non-shuffled", "maker": "leuven", "family": "leuven/robbertje", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A smaller Dutch encoder distilled from RobBERT for classification and tagging when the full teacher is unnecessary.", "summary_intro": "KU Leuven model RobBERTje 1GB non-shuffled (robbertje-1-gb-non-shuffled). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6241, "dossier_id": "leuven/robbertje/robbertje-1-gb-shuffled", "name": "RobBERTje 1GB shuffled", "maker": "leuven", "family": "leuven/robbertje", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A smaller Dutch RobBERT for fine-tuning, trained on shuffled OSCAR rather than the non-shuffled dump.", "summary_intro": "KU Leuven model RobBERTje 1GB shuffled (robbertje-1-gb-shuffled). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6242, "dossier_id": "lg/exaone/exaone-1-0", "name": "EXAONE 1.0", "maker": "lg", "family": "lg/exaone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "In-house expert multimodal system for LG affiliates, including chat and materials-related research. Not a public model.", "summary_intro": "EXAONE 1.0 LG AI Research released this as exaone. Status here is retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6243, "dossier_id": "lg/exaone/exaone-2-0", "name": "EXAONE 2.0", "maker": "lg", "family": "lg/exaone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "EXAONE 2.0 LG AI Research released this as exaone. Status here is retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6244, "dossier_id": "lg/exaone-path/exaone-path-1-0", "name": "EXAONE Path 1.0", "maker": "lg", "family": "lg/exaone-path", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Patch encoder for gigapixel histopathology slides. Downstream heads, not this file alone, do MSI and mutation tasks.", "summary_intro": "EXAONE Path 1.0 LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6245, "dossier_id": "lg/exaone-path/exaone-path-1-5", "name": "EXAONE Path 1.5", "maker": "lg", "family": "lg/exaone-path", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "EXAONE Path 1.5 LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6246, "dossier_id": "lg/exaone-path/exaone-path-2-0", "name": "EXAONE Path 2.0", "maker": "lg", "family": "lg/exaone-path", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pathology foundation model that learns patch features from slide-level labels, aimed at biomarker prediction from whole-slide images.", "summary_intro": "EXAONE Path 2.0 LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6247, "dossier_id": "lg/exaone-path/exaone-path-2-0-rev-egfr", "name": "EXAONE Path 2.0 rev EGFR", "maker": "lg", "family": "lg/exaone-path", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Revised slide model that outputs an EGFR mutation probability from a whole-slide image.", "summary_intro": "EXAONE Path 2.0 rev EGFR LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6248, "dossier_id": "lg/exaone-path/exaone-path-2-5", "name": "EXAONE Path 2.5", "maker": "lg", "family": "lg/exaone-path", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pathology foundation model that aligns slide images with genomic, epigenetic, and transcriptomic measurements.", "summary_intro": "EXAONE Path 2.5 LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6249, "dossier_id": "lg/exaone-path/exaone-path-crc-msi-predictor", "name": "EXAONE Path CRC MSI Predictor", "maker": "lg", "family": "lg/exaone-path", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reference model that predicts colorectal MSI status from an H&E slide using EXAONE Path 1.0 features.", "summary_intro": "EXAONE Path CRC MSI Predictor LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6250, "dossier_id": "lg/exaone-path/exaone-path-egfr", "name": "EXAONE Path LUAD-EGFR Predictor", "maker": "lg", "family": "lg/exaone-path", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reference model that predicts EGFR mutation status from a lung-adenocarcinoma H&E slide using EXAONE Path 2.0.", "summary_intro": "EXAONE Path LUAD-EGFR Predictor LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6251, "dossier_id": "lg/exaone-path/exaone-path-msi", "name": "EXAONE Path MSI", "maker": "lg", "family": "lg/exaone-path", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "EXAONE Path MSI LG AI Research released this as exaone-path. Status here is active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6252, "dossier_id": "liege/appa/appa", "name": "Appa", "maker": "liege", "family": "liege/appa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6253, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same 1-to-7 relevance scoring job as v1.0, with the input order swapped for cache-friendly document reranking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6254, "dossier_id": "lightblue/shitsu/shitsu-text-scorer", "name": "Shitsu", "maker": "lightblue", "family": "lightblue/shitsu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6255, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6256, "dossier_id": "lighton/alfred/alfred-4", "name": "Alfred 4", "maker": "lighton", "family": "lighton/alfred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6257, "dossier_id": "lighton/alfred/alfred-4-2", "name": "Alfred 4.2", "maker": "lighton", "family": "lighton/alfred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6258, "dossier_id": "lighton/alfred/alfred-40b-0723", "name": "Alfred-40B-0723", "maker": "lighton", "family": "lighton/alfred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6259, "dossier_id": "lighton/rerank/monoqwen2-vl-v0-1", "name": "MonoQwen2-VL-v0.1", "maker": "lighton", "family": "lighton/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6260, "dossier_id": "lighton/rita/rita-l", "name": "RITA-large", "maker": "lighton", "family": "lighton/rita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6261, "dossier_id": "lighton/rita/rita-m", "name": "RITA-medium", "maker": "lighton", "family": "lighton/rita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6262, "dossier_id": "lighton/rita/rita-s", "name": "RITA-small", "maker": "lighton", "family": "lighton/rita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6263, "dossier_id": "lighton/rita/rita-xl", "name": "RITA-xlarge", "maker": "lighton", "family": "lighton/rita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6264, "dossier_id": "lightricks/ltx/ltx-2", "name": "LTX-2", "maker": "lightricks", "family": "lightricks/ltx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6265, "dossier_id": "lightricks/ltx/ltx-2-3", "name": "LTX-2.3", "maker": "lightricks", "family": "lightricks/ltx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6266, "dossier_id": "lightricks/ltx/ltx-2-5", "name": "LTX-2.5", "maker": "lightricks", "family": "lightricks/ltx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6267, "dossier_id": "lightricks/ltx/ltx-video", "name": "LTX-Video", "maker": "lightricks", "family": "lightricks/ltx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6268, "dossier_id": "lilt/contextual-ai/v2", "name": "LILT-V2", "maker": "lilt", "family": "lilt/contextual-ai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6269, "dossier_id": "lilt/contextual-ai/v3", "name": "LILT-V3", "maker": "lilt", "family": "lilt/contextual-ai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6270, "dossier_id": "lilt/contextual-ai/v3-5", "name": "LILT-V3.5", "maker": "lilt", "family": "lilt/contextual-ai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6271, "dossier_id": "lilt/contextual-ai/v4", "name": "LILT V4.0", "maker": "lilt", "family": "lilt/contextual-ai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6272, "dossier_id": "lilt/emma/emma", "name": "Emma", "maker": "lilt", "family": "lilt/emma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6273, "dossier_id": "line/achoris/achoris", "name": "Achoris", "maker": "line", "family": "line/achoris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6274, "dossier_id": "line/chronaccret/chronaccret", "name": "ChronAccRet", "maker": "line", "family": "line/chronaccret", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6275, "dossier_id": "line/clip/clip-japanese-base", "name": "clip-japanese-base", "maker": "line", "family": "line/clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6276, "dossier_id": "line/clip/clip-japanese-base-v2", "name": "clip-japanese-base-v2", "maker": "line", "family": "line/clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6277, "dossier_id": "line/moegi/moegi-bert-110m", "name": "Moegi BERT 110M", "maker": "line", "family": "line/moegi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6278, "dossier_id": "line/motion-patches/motion-patches", "name": "Motion Patches", "maker": "line", "family": "line/motion-patches", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6279, "dossier_id": "line/mulan/japanese-mulan-base", "name": "Japanese MuLan Base", "maker": "line", "family": "line/mulan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6280, "dossier_id": "line/universe/universe", "name": "UNIVERSE", "maker": "line", "family": "line/universe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6281, "dossier_id": "line/universe/universe-plusplus", "name": "UNIVERSE++", "maker": "line", "family": "line/universe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6282, "dossier_id": "line/wavetrainerfit/wavetrainerfit", "name": "WaveTrainerFit", "maker": "line", "family": "line/wavetrainerfit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6283, "dossier_id": "line/yjvoice/yjvoice", "name": "YJVOICE", "maker": "line", "family": "line/yjvoice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6284, "dossier_id": "lingvanex/nmt/af-en", "name": "Lingvanex Afrikaans to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6285, "dossier_id": "lingvanex/nmt/am-en", "name": "Lingvanex Amharic to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6286, "dossier_id": "lingvanex/nmt/ar-en", "name": "Lingvanex Arabic to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6287, "dossier_id": "lingvanex/nmt/az-en", "name": "Lingvanex Azerbaijani to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6288, "dossier_id": "lingvanex/nmt/az-ru", "name": "Lingvanex Azerbaijani to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6289, "dossier_id": "lingvanex/nmt/be-en", "name": "Lingvanex Belarusian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6290, "dossier_id": "lingvanex/nmt/bg-en", "name": "Lingvanex Bulgarian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6291, "dossier_id": "lingvanex/nmt/bn-en", "name": "Lingvanex Bengali to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6292, "dossier_id": "lingvanex/nmt/bs-en", "name": "Lingvanex Bosnian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6293, "dossier_id": "lingvanex/nmt/ca-en", "name": "Lingvanex Catalan to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6294, "dossier_id": "lingvanex/nmt/ceb-en", "name": "Lingvanex Cebuano to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6295, "dossier_id": "lingvanex/nmt/co-en", "name": "Lingvanex Corsican to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6296, "dossier_id": "lingvanex/nmt/cs-en", "name": "Lingvanex Czech to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6297, "dossier_id": "lingvanex/nmt/cy-en", "name": "Lingvanex Welsh to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6298, "dossier_id": "lingvanex/nmt/da-en", "name": "Lingvanex Danish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6299, "dossier_id": "lingvanex/nmt/de-en", "name": "Lingvanex German to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6300, "dossier_id": "lingvanex/nmt/el-en", "name": "Lingvanex Greek to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6301, "dossier_id": "lingvanex/nmt/en-af", "name": "Lingvanex English to Afrikaans", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6302, "dossier_id": "lingvanex/nmt/en-am", "name": "Lingvanex English to Amharic", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6303, "dossier_id": "lingvanex/nmt/en-ar", "name": "Lingvanex English to Arabic", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6304, "dossier_id": "lingvanex/nmt/en-az", "name": "Lingvanex English to Azerbaijani", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6305, "dossier_id": "lingvanex/nmt/en-be", "name": "Lingvanex English to Belarusian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6306, "dossier_id": "lingvanex/nmt/en-bg", "name": "Lingvanex English to Bulgarian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6307, "dossier_id": "lingvanex/nmt/en-bn", "name": "Lingvanex English to Bengali", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6308, "dossier_id": "lingvanex/nmt/en-bs", "name": "Lingvanex English to Bosnian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6309, "dossier_id": "lingvanex/nmt/en-ca", "name": "Lingvanex English to Catalan", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6310, "dossier_id": "lingvanex/nmt/en-ceb", "name": "Lingvanex English to Cebuano", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6311, "dossier_id": "lingvanex/nmt/en-co", "name": "Lingvanex English to Corsican", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6312, "dossier_id": "lingvanex/nmt/en-cs", "name": "Lingvanex English to Czech", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6313, "dossier_id": "lingvanex/nmt/en-cy", "name": "Lingvanex English to Welsh", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6314, "dossier_id": "lingvanex/nmt/en-da", "name": "Lingvanex English to Danish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6315, "dossier_id": "lingvanex/nmt/en-de", "name": "Lingvanex English to German", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6316, "dossier_id": "lingvanex/nmt/en-el", "name": "Lingvanex English to Greek", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6317, "dossier_id": "lingvanex/nmt/en-eo", "name": "Lingvanex English to Esperanto", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6318, "dossier_id": "lingvanex/nmt/en-es", "name": "Lingvanex English to Spanish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6319, "dossier_id": "lingvanex/nmt/en-et", "name": "Lingvanex English to Estonian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6320, "dossier_id": "lingvanex/nmt/en-eu", "name": "Lingvanex English to Basque", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6321, "dossier_id": "lingvanex/nmt/en-fa", "name": "Lingvanex English to Persian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6322, "dossier_id": "lingvanex/nmt/en-fi", "name": "Lingvanex English to Finnish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6323, "dossier_id": "lingvanex/nmt/en-fil", "name": "Lingvanex English to Filipino", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6324, "dossier_id": "lingvanex/nmt/en-fr", "name": "Lingvanex English to French", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6325, "dossier_id": "lingvanex/nmt/en-fy", "name": "Lingvanex English to Frisian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6326, "dossier_id": "lingvanex/nmt/en-ga", "name": "Lingvanex English to Irish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6327, "dossier_id": "lingvanex/nmt/en-gd", "name": "Lingvanex English to Gaelic", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6328, "dossier_id": "lingvanex/nmt/en-gl", "name": "Lingvanex English to Galician", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6329, "dossier_id": "lingvanex/nmt/en-gu", "name": "Lingvanex English to Gujarati", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6330, "dossier_id": "lingvanex/nmt/en-ha", "name": "Lingvanex English to Hausa", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6331, "dossier_id": "lingvanex/nmt/en-haw", "name": "Lingvanex English to Hawaiian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6332, "dossier_id": "lingvanex/nmt/en-he", "name": "Lingvanex English to Hebrew", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6333, "dossier_id": "lingvanex/nmt/en-hi", "name": "Lingvanex English to Hindi", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6334, "dossier_id": "lingvanex/nmt/en-hmn", "name": "Lingvanex English to Hmong", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6335, "dossier_id": "lingvanex/nmt/en-hr", "name": "Lingvanex English to Croatian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6336, "dossier_id": "lingvanex/nmt/en-ht", "name": "Lingvanex English to Haitian Creole", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6337, "dossier_id": "lingvanex/nmt/en-hu", "name": "Lingvanex English to Hungarian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6338, "dossier_id": "lingvanex/nmt/en-hy", "name": "Lingvanex English to Armenian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6339, "dossier_id": "lingvanex/nmt/en-id", "name": "Lingvanex English to Indonesian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6340, "dossier_id": "lingvanex/nmt/en-ig", "name": "Lingvanex English to Igbo", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6341, "dossier_id": "lingvanex/nmt/en-is", "name": "Lingvanex English to Icelandic", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6342, "dossier_id": "lingvanex/nmt/en-it", "name": "Lingvanex English to Italian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6343, "dossier_id": "lingvanex/nmt/en-ja", "name": "Lingvanex English to Japanese", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6344, "dossier_id": "lingvanex/nmt/en-jv", "name": "Lingvanex English to Javanese", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6345, "dossier_id": "lingvanex/nmt/en-ka", "name": "Lingvanex English to Georgian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6346, "dossier_id": "lingvanex/nmt/en-kk", "name": "Lingvanex English to Kazakh", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6347, "dossier_id": "lingvanex/nmt/en-km", "name": "Lingvanex English to Khmer", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6348, "dossier_id": "lingvanex/nmt/en-kn", "name": "Lingvanex English to Kannada", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6349, "dossier_id": "lingvanex/nmt/en-ko", "name": "Lingvanex English to Korean", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6350, "dossier_id": "lingvanex/nmt/en-ku", "name": "Lingvanex English to Kurdish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6351, "dossier_id": "lingvanex/nmt/en-ky", "name": "Lingvanex English to Kyrgyz", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6352, "dossier_id": "lingvanex/nmt/en-la", "name": "Lingvanex English to Latin", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6353, "dossier_id": "lingvanex/nmt/en-lb", "name": "Lingvanex English to Luxembourgish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6354, "dossier_id": "lingvanex/nmt/en-lo", "name": "Lingvanex English to Lao", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6355, "dossier_id": "lingvanex/nmt/en-lt", "name": "Lingvanex English to Lithuanian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6356, "dossier_id": "lingvanex/nmt/en-lv", "name": "Lingvanex English to Latvian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6357, "dossier_id": "lingvanex/nmt/en-mg", "name": "Lingvanex English to Malagasy", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6358, "dossier_id": "lingvanex/nmt/en-mi", "name": "Lingvanex English to Maori", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6359, "dossier_id": "lingvanex/nmt/en-mk", "name": "Lingvanex English to Macedonian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6360, "dossier_id": "lingvanex/nmt/en-ml", "name": "Lingvanex English to Malayalam", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6361, "dossier_id": "lingvanex/nmt/en-mn", "name": "Lingvanex English to Mongolian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6362, "dossier_id": "lingvanex/nmt/en-mr", "name": "Lingvanex English to Marathi", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6363, "dossier_id": "lingvanex/nmt/en-ms", "name": "Lingvanex English to Malay", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6364, "dossier_id": "lingvanex/nmt/en-mt", "name": "Lingvanex English to Maltese", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6365, "dossier_id": "lingvanex/nmt/en-my", "name": "Lingvanex English to Myanmar", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6366, "dossier_id": "lingvanex/nmt/en-ne", "name": "Lingvanex English to Nepali", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6367, "dossier_id": "lingvanex/nmt/en-nl", "name": "Lingvanex English to Dutch", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6368, "dossier_id": "lingvanex/nmt/en-no", "name": "Lingvanex English to Norwegian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6369, "dossier_id": "lingvanex/nmt/en-ny", "name": "Lingvanex English to Chichewa", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6370, "dossier_id": "lingvanex/nmt/en-or", "name": "Lingvanex English to Odia", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6371, "dossier_id": "lingvanex/nmt/en-pa", "name": "Lingvanex English to Punjabi", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6372, "dossier_id": "lingvanex/nmt/en-pl", "name": "Lingvanex English to Polish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6373, "dossier_id": "lingvanex/nmt/en-ps", "name": "Lingvanex English to Pushto", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6374, "dossier_id": "lingvanex/nmt/en-pt", "name": "Lingvanex English to Portuguese", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6375, "dossier_id": "lingvanex/nmt/en-pt-br", "name": "Lingvanex English to Portuguese (Brazil)", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6376, "dossier_id": "lingvanex/nmt/en-ro", "name": "Lingvanex English to Romanian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6377, "dossier_id": "lingvanex/nmt/en-ru", "name": "Lingvanex English to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6378, "dossier_id": "lingvanex/nmt/en-rw", "name": "Lingvanex English to Kinyarwanda", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6379, "dossier_id": "lingvanex/nmt/en-sd", "name": "Lingvanex English to Sindhi", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6380, "dossier_id": "lingvanex/nmt/en-si", "name": "Lingvanex English to Sinhala", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6381, "dossier_id": "lingvanex/nmt/en-sk", "name": "Lingvanex English to Slovak", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6382, "dossier_id": "lingvanex/nmt/en-sl", "name": "Lingvanex English to Slovenian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6383, "dossier_id": "lingvanex/nmt/en-sm", "name": "Lingvanex English to Samoan", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6384, "dossier_id": "lingvanex/nmt/en-sn", "name": "Lingvanex English to Shona", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6385, "dossier_id": "lingvanex/nmt/en-so", "name": "Lingvanex English to Somali", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6386, "dossier_id": "lingvanex/nmt/en-sq", "name": "Lingvanex English to Albanian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6387, "dossier_id": "lingvanex/nmt/en-sr", "name": "Lingvanex English to Serbian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6388, "dossier_id": "lingvanex/nmt/en-st", "name": "Lingvanex English to Sesotho", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6389, "dossier_id": "lingvanex/nmt/en-su", "name": "Lingvanex English to Sundanese", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6390, "dossier_id": "lingvanex/nmt/en-sv", "name": "Lingvanex English to Swedish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6391, "dossier_id": "lingvanex/nmt/en-sw", "name": "Lingvanex English to Swahili", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6392, "dossier_id": "lingvanex/nmt/en-ta", "name": "Lingvanex English to Tamil", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6393, "dossier_id": "lingvanex/nmt/en-te", "name": "Lingvanex English to Telugu", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6394, "dossier_id": "lingvanex/nmt/en-tg", "name": "Lingvanex English to Tajik", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6395, "dossier_id": "lingvanex/nmt/en-th", "name": "Lingvanex English to Thai", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6396, "dossier_id": "lingvanex/nmt/en-tk", "name": "Lingvanex English to Turkmen", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6397, "dossier_id": "lingvanex/nmt/en-tr", "name": "Lingvanex English to Turkish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6398, "dossier_id": "lingvanex/nmt/en-tt", "name": "Lingvanex English to Tatar", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6399, "dossier_id": "lingvanex/nmt/en-ug", "name": "Lingvanex English to Uyghur", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6400, "dossier_id": "lingvanex/nmt/en-uk", "name": "Lingvanex English to Ukrainian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6401, "dossier_id": "lingvanex/nmt/en-ur", "name": "Lingvanex English to Urdu", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6402, "dossier_id": "lingvanex/nmt/en-uz", "name": "Lingvanex English to Uzbek", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6403, "dossier_id": "lingvanex/nmt/en-vi", "name": "Lingvanex English to Vietnamese", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6404, "dossier_id": "lingvanex/nmt/en-xh", "name": "Lingvanex English to Xhosa", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6405, "dossier_id": "lingvanex/nmt/en-yi", "name": "Lingvanex English to Yiddish", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6406, "dossier_id": "lingvanex/nmt/en-yo", "name": "Lingvanex English to Yoruba", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6407, "dossier_id": "lingvanex/nmt/en-zh-hans", "name": "Lingvanex English to Chinese (Simplified)", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6408, "dossier_id": "lingvanex/nmt/en-zh-hant", "name": "Lingvanex English to Chinese (Traditional)", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6409, "dossier_id": "lingvanex/nmt/en-zu", "name": "Lingvanex English to Zulu", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6410, "dossier_id": "lingvanex/nmt/eo-en", "name": "Lingvanex Esperanto to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6411, "dossier_id": "lingvanex/nmt/es-en", "name": "Lingvanex Spanish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6412, "dossier_id": "lingvanex/nmt/et-en", "name": "Lingvanex Estonian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6413, "dossier_id": "lingvanex/nmt/eu-en", "name": "Lingvanex Basque to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6414, "dossier_id": "lingvanex/nmt/fa-en", "name": "Lingvanex Persian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6415, "dossier_id": "lingvanex/nmt/fi-en", "name": "Lingvanex Finnish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6416, "dossier_id": "lingvanex/nmt/fil-en", "name": "Lingvanex Filipino to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6417, "dossier_id": "lingvanex/nmt/fr-en", "name": "Lingvanex French to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6418, "dossier_id": "lingvanex/nmt/fy-en", "name": "Lingvanex Frisian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6419, "dossier_id": "lingvanex/nmt/ga-en", "name": "Lingvanex Irish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6420, "dossier_id": "lingvanex/nmt/gd-en", "name": "Lingvanex Gaelic to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6421, "dossier_id": "lingvanex/nmt/gl-en", "name": "Lingvanex Galician to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6422, "dossier_id": "lingvanex/nmt/gu-en", "name": "Lingvanex Gujarati to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6423, "dossier_id": "lingvanex/nmt/ha-en", "name": "Lingvanex Hausa to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6424, "dossier_id": "lingvanex/nmt/haw-en", "name": "Lingvanex Hawaiian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6425, "dossier_id": "lingvanex/nmt/he-en", "name": "Lingvanex Hebrew to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6426, "dossier_id": "lingvanex/nmt/hi-en", "name": "Lingvanex Hindi to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6427, "dossier_id": "lingvanex/nmt/hmn-en", "name": "Lingvanex Hmong to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6428, "dossier_id": "lingvanex/nmt/hr-en", "name": "Lingvanex Croatian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6429, "dossier_id": "lingvanex/nmt/ht-en", "name": "Lingvanex Haitian Creole to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6430, "dossier_id": "lingvanex/nmt/hu-en", "name": "Lingvanex Hungarian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6431, "dossier_id": "lingvanex/nmt/hy-en", "name": "Lingvanex Armenian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6432, "dossier_id": "lingvanex/nmt/hy-ru", "name": "Lingvanex Armenian to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6433, "dossier_id": "lingvanex/nmt/id-en", "name": "Lingvanex Indonesian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6434, "dossier_id": "lingvanex/nmt/ig-en", "name": "Lingvanex Igbo to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6435, "dossier_id": "lingvanex/nmt/is-en", "name": "Lingvanex Icelandic to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6436, "dossier_id": "lingvanex/nmt/it-en", "name": "Lingvanex Italian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6437, "dossier_id": "lingvanex/nmt/ja-en", "name": "Lingvanex Japanese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6438, "dossier_id": "lingvanex/nmt/jv-en", "name": "Lingvanex Javanese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6439, "dossier_id": "lingvanex/nmt/ka-en", "name": "Lingvanex Georgian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6440, "dossier_id": "lingvanex/nmt/ka-ru", "name": "Lingvanex Georgian to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6441, "dossier_id": "lingvanex/nmt/kk-en", "name": "Lingvanex Kazakh to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6442, "dossier_id": "lingvanex/nmt/kk-ru", "name": "Lingvanex Kazakh to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6443, "dossier_id": "lingvanex/nmt/km-en", "name": "Lingvanex Khmer to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6444, "dossier_id": "lingvanex/nmt/kn-en", "name": "Lingvanex Kannada to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6445, "dossier_id": "lingvanex/nmt/ko-en", "name": "Lingvanex Korean to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6446, "dossier_id": "lingvanex/nmt/ku-en", "name": "Lingvanex Kurdish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6447, "dossier_id": "lingvanex/nmt/ky-en", "name": "Lingvanex Kyrgyz to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6448, "dossier_id": "lingvanex/nmt/ky-ru", "name": "Lingvanex Kyrgyz to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6449, "dossier_id": "lingvanex/nmt/la-en", "name": "Lingvanex Latin to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6450, "dossier_id": "lingvanex/nmt/lb-en", "name": "Lingvanex Luxembourgish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6451, "dossier_id": "lingvanex/nmt/lo-en", "name": "Lingvanex Lao to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6452, "dossier_id": "lingvanex/nmt/lt-en", "name": "Lingvanex Lithuanian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6453, "dossier_id": "lingvanex/nmt/lv-en", "name": "Lingvanex Latvian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6454, "dossier_id": "lingvanex/nmt/mg-en", "name": "Lingvanex Malagasy to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6455, "dossier_id": "lingvanex/nmt/mi-en", "name": "Lingvanex Maori to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6456, "dossier_id": "lingvanex/nmt/mk-en", "name": "Lingvanex Macedonian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6457, "dossier_id": "lingvanex/nmt/ml-en", "name": "Lingvanex Malayalam to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6458, "dossier_id": "lingvanex/nmt/mn-en", "name": "Lingvanex Mongolian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6459, "dossier_id": "lingvanex/nmt/mr-en", "name": "Lingvanex Marathi to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6460, "dossier_id": "lingvanex/nmt/ms-en", "name": "Lingvanex Malay to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6461, "dossier_id": "lingvanex/nmt/mt-en", "name": "Lingvanex Maltese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6462, "dossier_id": "lingvanex/nmt/my-en", "name": "Lingvanex Myanmar to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6463, "dossier_id": "lingvanex/nmt/ne-en", "name": "Lingvanex Nepali to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6464, "dossier_id": "lingvanex/nmt/nl-en", "name": "Lingvanex Dutch to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6465, "dossier_id": "lingvanex/nmt/no-en", "name": "Lingvanex Norwegian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6466, "dossier_id": "lingvanex/nmt/ny-en", "name": "Lingvanex Chichewa to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6467, "dossier_id": "lingvanex/nmt/or-en", "name": "Lingvanex Odia to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6468, "dossier_id": "lingvanex/nmt/pa-en", "name": "Lingvanex Punjabi to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6469, "dossier_id": "lingvanex/nmt/pl-en", "name": "Lingvanex Polish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6470, "dossier_id": "lingvanex/nmt/ps-en", "name": "Lingvanex Pushto to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6471, "dossier_id": "lingvanex/nmt/pt-br-en", "name": "Lingvanex Portuguese (Brazil) to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6472, "dossier_id": "lingvanex/nmt/pt-en", "name": "Lingvanex Portuguese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6473, "dossier_id": "lingvanex/nmt/ro-en", "name": "Lingvanex Romanian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6474, "dossier_id": "lingvanex/nmt/ru-az", "name": "Lingvanex Russian to Azerbaijani", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6475, "dossier_id": "lingvanex/nmt/ru-be", "name": "Lingvanex Russian to Belarusian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6476, "dossier_id": "lingvanex/nmt/ru-en", "name": "Lingvanex Russian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6477, "dossier_id": "lingvanex/nmt/ru-hy", "name": "Lingvanex Russian to Armenian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6478, "dossier_id": "lingvanex/nmt/ru-ka", "name": "Lingvanex Russian to Georgian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6479, "dossier_id": "lingvanex/nmt/ru-kk", "name": "Lingvanex Russian to Kazakh", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6480, "dossier_id": "lingvanex/nmt/ru-ky", "name": "Lingvanex Russian to Kyrgyz", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6481, "dossier_id": "lingvanex/nmt/ru-tg", "name": "Lingvanex Russian to Tajik", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6482, "dossier_id": "lingvanex/nmt/ru-uk", "name": "Lingvanex Russian to Ukrainian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6483, "dossier_id": "lingvanex/nmt/ru-uz", "name": "Lingvanex Russian to Uzbek", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6484, "dossier_id": "lingvanex/nmt/rw-en", "name": "Lingvanex Kinyarwanda to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6485, "dossier_id": "lingvanex/nmt/sd-en", "name": "Lingvanex Sindhi to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6486, "dossier_id": "lingvanex/nmt/si-en", "name": "Lingvanex Sinhala to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6487, "dossier_id": "lingvanex/nmt/sk-en", "name": "Lingvanex Slovak to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6488, "dossier_id": "lingvanex/nmt/sl-en", "name": "Lingvanex Slovenian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6489, "dossier_id": "lingvanex/nmt/sm-en", "name": "Lingvanex Samoan to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6490, "dossier_id": "lingvanex/nmt/sn-en", "name": "Lingvanex Shona to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6491, "dossier_id": "lingvanex/nmt/so-en", "name": "Lingvanex Somali to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6492, "dossier_id": "lingvanex/nmt/sq-en", "name": "Lingvanex Albanian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6493, "dossier_id": "lingvanex/nmt/sr-en", "name": "Lingvanex Serbian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6494, "dossier_id": "lingvanex/nmt/st-en", "name": "Lingvanex Sesotho to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6495, "dossier_id": "lingvanex/nmt/su-en", "name": "Lingvanex Sundanese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6496, "dossier_id": "lingvanex/nmt/sv-en", "name": "Lingvanex Swedish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6497, "dossier_id": "lingvanex/nmt/sw-en", "name": "Lingvanex Swahili to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6498, "dossier_id": "lingvanex/nmt/ta-en", "name": "Lingvanex Tamil to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6499, "dossier_id": "lingvanex/nmt/te-en", "name": "Lingvanex Telugu to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6500, "dossier_id": "lingvanex/nmt/tg-en", "name": "Lingvanex Tajik to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6501, "dossier_id": "lingvanex/nmt/tg-ru", "name": "Lingvanex Tajik to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6502, "dossier_id": "lingvanex/nmt/th-en", "name": "Lingvanex Thai to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6503, "dossier_id": "lingvanex/nmt/tk-en", "name": "Lingvanex Turkmen to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6504, "dossier_id": "lingvanex/nmt/tr-en", "name": "Lingvanex Turkish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6505, "dossier_id": "lingvanex/nmt/tt-en", "name": "Lingvanex Tatar to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6506, "dossier_id": "lingvanex/nmt/ug-en", "name": "Lingvanex Uyghur to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6507, "dossier_id": "lingvanex/nmt/uk-en", "name": "Lingvanex Ukrainian to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6508, "dossier_id": "lingvanex/nmt/uk-ru", "name": "Lingvanex Ukrainian to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6509, "dossier_id": "lingvanex/nmt/ur-en", "name": "Lingvanex Urdu to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6510, "dossier_id": "lingvanex/nmt/uz-en", "name": "Lingvanex Uzbek to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6511, "dossier_id": "lingvanex/nmt/uz-ru", "name": "Lingvanex Uzbek to Russian", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6512, "dossier_id": "lingvanex/nmt/vi-en", "name": "Lingvanex Vietnamese to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6513, "dossier_id": "lingvanex/nmt/xh-en", "name": "Lingvanex Xhosa to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6514, "dossier_id": "lingvanex/nmt/yi-en", "name": "Lingvanex Yiddish to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6515, "dossier_id": "lingvanex/nmt/yo-en", "name": "Lingvanex Yoruba to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6516, "dossier_id": "lingvanex/nmt/zh-hans-en", "name": "Lingvanex Chinese (Simplified) to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6517, "dossier_id": "lingvanex/nmt/zh-hant-en", "name": "Lingvanex Chinese (Traditional) to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6518, "dossier_id": "lingvanex/nmt/zu-en", "name": "Lingvanex Zulu to English", "maker": "lingvanex", "family": "lingvanex/nmt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6519, "dossier_id": "lingvanex/speech/speech-recognition", "name": "Lingvanex Speech Recognition", "maker": "lingvanex", "family": "lingvanex/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6520, "dossier_id": "linkoping/atom/atom", "name": "ATOM", "maker": "linkoping", "family": "linkoping/atom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6521, "dossier_id": "linkoping/dedode/dedode", "name": "DeDoDe", "maker": "linkoping", "family": "linkoping/dedode", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6522, "dossier_id": "linkoping/dkm/dkm", "name": "DKM", "maker": "linkoping", "family": "linkoping/dkm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6523, "dossier_id": "linkoping/roma/roma", "name": "RoMa", "maker": "linkoping", "family": "linkoping/roma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6524, "dossier_id": "linkoping/roma/roma-v2", "name": "RoMa v2", "maker": "linkoping", "family": "linkoping/roma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6525, "dossier_id": "linkoping/roma/tiny-roma", "name": "Tiny RoMa", "maker": "linkoping", "family": "linkoping/roma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The README offers it as a smaller outdoor matcher. The only named loader is `tiny_roma_v1_outdoor`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6526, "dossier_id": "linz/beat-this/beat-this", "name": "Beat This!", "maker": "linz", "family": "linz/beat-this", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6527, "dossier_id": "linz/beat-this/beat-this-small", "name": "Beat This! small", "maker": "linz", "family": "linz/beat-this", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6528, "dossier_id": "linz/bio-xlstm/prot-xlstm-102m", "name": "Prot-xLSTM-102M", "maker": "linz", "family": "linz/bio-xlstm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6529, "dossier_id": "linz/bio-xlstm/prot-xlstm-26m", "name": "Prot-xLSTM-26M", "maker": "linz", "family": "linz/bio-xlstm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6530, "dossier_id": "linz/gyroswin/gyroswin-large", "name": "GyroSwin Large", "maker": "linz", "family": "linz/gyroswin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6531, "dossier_id": "linz/gyroswin/gyroswin-medium", "name": "GyroSwin Medium", "maker": "linz", "family": "linz/gyroswin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6532, "dossier_id": "linz/gyroswin/gyroswin-small", "name": "GyroSwin Small", "maker": "linz", "family": "linz/gyroswin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6533, "dossier_id": "linz/pa-audioic/pa-audioic", "name": "PAAudioIC", "maker": "linz", "family": "linz/pa-audioic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6534, "dossier_id": "linz/passt/passt-s", "name": "PaSST-S", "maker": "linz", "family": "linz/passt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6535, "dossier_id": "liquid-ai/d1/d1", "name": "d1", "maker": "liquid-ai", "family": "liquid-ai/d1", "context_length": null, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6536, "dossier_id": "liquid-ai/d1/d1-ultra", "name": "d1-ultra", "maker": "liquid-ai", "family": "liquid-ai/d1", "context_length": null, "prompt_price_per_m": 0.042, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6537, "dossier_id": "liquid-ai/dspark/lfm2-5-1-2b-instruct-dspark", "name": "LFM2.5-1.2B-Instruct-DSpark", "maker": "liquid-ai", "family": "liquid-ai/dspark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6538, "dossier_id": "liquid-ai/dspark/lfm2-5-2-6b-dspark", "name": "LFM2.5-2.6B-DSpark", "maker": "liquid-ai", "family": "liquid-ai/dspark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6539, "dossier_id": "liquid-ai/dspark/lfm2-5-8b-a1b-dspark", "name": "LFM2.5-8B-A1B-DSpark", "maker": "liquid-ai", "family": "liquid-ai/dspark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6540, "dossier_id": "liquid-ai/dspark/lfm2-5-vl-3b-dspark", "name": "LFM2.5-VL-3B-DSpark", "maker": "liquid-ai", "family": "liquid-ai/dspark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6541, "dossier_id": "liquid-ai/lfm/lfm-1b", "name": "LFM-1B", "maker": "liquid-ai", "family": "liquid-ai/lfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6542, "dossier_id": "liquid-ai/lfm/lfm-3b", "name": "LFM-3B", "maker": "liquid-ai", "family": "liquid-ai/lfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6543, "dossier_id": "liquid-ai/lfm/lfm-40b", "name": "LFM-40B", "maker": "liquid-ai", "family": "liquid-ai/lfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6544, "dossier_id": "liquid-ai/lfm/lfm-7b", "name": "LFM-7B", "maker": "liquid-ai", "family": "liquid-ai/lfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6545, "dossier_id": "liquid-ai/lfm2-5/lfm2-5-1-2b-jp", "name": "LFM2.5-1.2B-JP", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6546, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6547, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6548, "dossier_id": "liquid-ai/lfm2-5-nano/lfm2-5-colbert-350m", "name": "LFM2.5-ColBERT-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-5-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6549, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6550, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6551, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Rule-matching head that scores every token against free-text policy rules in one encoder pass.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6552, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot routing head that scores a prompt against user-defined routing lanes in one encoder pass.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6553, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6554, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6555, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller document-extraction vision model. Vendor table: JSON validity 98.9, F1 98.8, VLM judge 84.5.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6556, "dossier_id": "liquid-ai/lfm2-audio/lfm2-audio-1-5b", "name": "LFM2-Audio-1.5B", "maker": "liquid-ai", "family": "liquid-ai/lfm2-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6557, "dossier_id": "liquid-ai/lfm2-nano/lfm2-1-2b-extract", "name": "LFM2-1.2B-Extract", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6558, "dossier_id": "liquid-ai/lfm2-nano/lfm2-1-2b-rag", "name": "LFM2-1.2B-RAG", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval-grounded answering model in the Nanos set. Deprecated. Docs suggest LFM2.5-1.2B-Instruct.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6559, "dossier_id": "liquid-ai/lfm2-nano/lfm2-1-2b-tool", "name": "LFM2-1.2B-Tool", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6560, "dossier_id": "liquid-ai/lfm2-nano/lfm2-2-6b-mmai", "name": "LFM2-2.6B-MMAI", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6561, "dossier_id": "liquid-ai/lfm2-nano/lfm2-2-6b-transcript", "name": "LFM2-2.6B-Transcript", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6562, "dossier_id": "liquid-ai/lfm2-nano/lfm2-350m-enjp-mt", "name": "LFM2-350M-ENJP-MT", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-Japanese translation Nano. Still in the current library with GGUF, MLX, and ONNX.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6563, "dossier_id": "liquid-ai/lfm2-nano/lfm2-350m-extract", "name": "LFM2-350M-Extract", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller extraction model in the Liquid Nanos set. Deprecated in favor of LFM2.5-350M.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6564, "dossier_id": "liquid-ai/lfm2-nano/lfm2-350m-math", "name": "LFM2-350M-Math", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6565, "dossier_id": "liquid-ai/lfm2-nano/lfm2-350m-pii-extract-jp", "name": "LFM2-350M-PII-Extract-JP", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6566, "dossier_id": "liquid-ai/lfm2-nano/lfm2-colbert-350m", "name": "LFM2-ColBERT-350M", "maker": "liquid-ai", "family": "liquid-ai/lfm2-nano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6567, "dossier_id": "lisbon/albertina/albertina-100m-ptbr", "name": "Albertina 100M PT-BR", "maker": "lisbon", "family": "lisbon/albertina", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6568, "dossier_id": "lisbon/albertina/albertina-1b5-ptbr", "name": "Albertina 1.5B PT-BR", "maker": "lisbon", "family": "lisbon/albertina", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6569, "dossier_id": "lisbon/albertina/albertina-900m-ptbr", "name": "Albertina 900M PT-BR (no brWaC)", "maker": "lisbon", "family": "lisbon/albertina", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6570, "dossier_id": "lisbon/albertina/albertina-900m-ptbr-brwac", "name": "Albertina 900M PT-BR (brWaC)", "maker": "lisbon", "family": "lisbon/albertina", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6571, "dossier_id": "lisbon/gervasio/gervasio-7b-ptbr", "name": "Gerv\u00e1sio 7B PT-BR", "maker": "lisbon", "family": "lisbon/gervasio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6572, "dossier_id": "ljubljana/codemix/muril-en-hi-codemixed", "name": "muril-en-hi-codemixed", "maker": "ljubljana", "family": "ljubljana/codemix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked modeling of code-mixed English and Hindi.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6573, "dossier_id": "ljubljana/codemix/roberta-en-hi-codemixed", "name": "roberta-en-hi-codemixed", "maker": "ljubljana", "family": "ljubljana/codemix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Masked modeling of conversational English, Hindi, and code-mixed text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6574, "dossier_id": "ljubljana/crosloengual/crosloengual-bert-si-nli", "name": "crosloengual-bert-si-nli", "maker": "ljubljana", "family": "ljubljana/crosloengual", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene natural language inference using the trilingual encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6575, "dossier_id": "ljubljana/elmo/elmo", "name": "ELMo embeddings", "maker": "ljubljana", "family": "ljubljana/elmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contextual word embeddings, including out-of-vocabulary words, for the seven languages named on the CLARIN record.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6576, "dossier_id": "ljubljana/gec/gemma-3-27b-it-dassle-gec", "name": "gemma-3-27b-it-dassle-gec", "maker": "ljubljana", "family": "ljubljana/gec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Correct Slovene grammar without unnecessary paraphrase. The user sends a sentence and the model returns the corrected sentence.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6577, "dossier_id": "ljubljana/sloberta/sloberta-sentinews-sentence", "name": "sloberta-sentinews-sentence", "maker": "ljubljana", "family": "ljubljana/sloberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Three-class sentence sentiment for Slovene news.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6578, "dossier_id": "ljubljana/sloberta/sloberta-si-nli", "name": "sloberta-si-nli", "maker": "ljubljana", "family": "ljubljana/sloberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene natural language inference on sentence pairs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6579, "dossier_id": "ljubljana/sloberta/sloberta-slo-word-spelling-annotator", "name": "SloBERTa spelling annotator", "maker": "ljubljana", "family": "ljubljana/sloberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Marking Slovene spelling errors and split or join mistakes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6580, "dossier_id": "ljubljana/sloberta/sloberta-word-case", "name": "sloberta-word-case-classification-multilabel", "maker": "ljubljana", "family": "ljubljana/sloberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Restoring Slovene word case from fully lowercased text, with a coarse reason label.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6581, "dossier_id": "ljubljana/sloner/sloner", "name": "SloNER 1.0", "maker": "ljubljana", "family": "ljubljana/sloner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene named entity recognition. A web demo is on slovenscina.eu.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6582, "dossier_id": "ljubljana/spelling/bertic-cro-word-spelling-annotator", "name": "BERTi\u0107 Croatian spelling annotator", "maker": "ljubljana", "family": "ljubljana/spelling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Marking misspelled Croatian words.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6583, "dossier_id": "ljubljana/summarizer/slo-summarizer", "name": "Slovenian text summarization models", "maker": "ljubljana", "family": "ljubljana/summarizer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Extractive, abstractive, and hybrid summarization of Slovene text, with a demo on slovenscina.eu.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6584, "dossier_id": "ljubljana/svila/svila-1-12b", "name": "SVILA-1-12B", "maker": "ljubljana", "family": "ljubljana/svila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene visual question answering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6585, "dossier_id": "ljubljana/svila/svila-1-4b", "name": "SVILA-1-4B", "maker": "ljubljana", "family": "ljubljana/svila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene visual question answering, by the description on the 12B SVILA card and the shared CLARIN dataset.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6586, "dossier_id": "ljubljana/t5/t5-sl-large", "name": "T5-sl-large", "maker": "ljubljana", "family": "ljubljana/t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Slovene sequence-to-sequence generation. The paper positions it as more useful for generation than for classification, where SloBERTa leads.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6587, "dossier_id": "ljubljana/t5/t5-sl-small", "name": "T5-sl-small", "maker": "ljubljana", "family": "ljubljana/t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6588, "dossier_id": "ljubljana/t5/t5-slo-word-form-corrector", "name": "T5-slo-word-form-corrector", "maker": "ljubljana", "family": "ljubljana/t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Correcting Slovene word shape.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6589, "dossier_id": "ljubljana/t5/t5-slo-word-order-corrector", "name": "T5-slo-word-order-corrector", "maker": "ljubljana", "family": "ljubljana/t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Correcting Slovene word order.", "summary_intro": "This is a Slovene T5 word-order corrector from CJVT at the University of Ljubljana. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6590, "dossier_id": "ljubljana/t5/t5-slo-word-spelling-corrector", "name": "T5-slo-word-spelling-corrector", "maker": "ljubljana", "family": "ljubljana/t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Correcting misspelled Slovene words.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6591, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6592, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruct4 tune of the 32K checkpoint. Not listed on the release index fetched on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6593, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Announced successor-scale MoE. Not a shipped checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6594, "dossier_id": "llm-jp/llm-jp-moshi/llm-jp-moshi-v1", "name": "LLM-jp-Moshi v1", "maker": "llm-jp", "family": "llm-jp/llm-jp-moshi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Spoken Japanese dialogue, not a text chatbot. Run with moshi.server and the Hub repo id.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6595, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-and-text instruction model for Japanese and English. The card's example asks the model to describe an image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6596, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6597, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6598, "dossier_id": "lmnt/aurora/aurora", "name": "Aurora", "maker": "lmnt", "family": "lmnt/aurora", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6599, "dossier_id": "lmnt/blizzard/blizzard", "name": "Blizzard", "maker": "lmnt", "family": "lmnt/blizzard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6600, "dossier_id": "lmu/cleandift/cleandift", "name": "CleanDIFT", "maker": "lmu", "family": "lmu/cleandift", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6601, "dossier_id": "lmu/depth-fm/depth-fm", "name": "DepthFM", "maker": "lmu", "family": "lmu/depth-fm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6602, "dossier_id": "lmu/dismo/dismo", "name": "DisMo", "maker": "lmu", "family": "lmu/dismo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6603, "dossier_id": "lmu/fpt/fpt", "name": "Flow Poke Transformer", "maker": "lmu", "family": "lmu/fpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6604, "dossier_id": "lmu/freud/freud", "name": "FREUD", "maker": "lmu", "family": "lmu/freud", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6605, "dossier_id": "lmu/garfield/garfield", "name": "GARFIELD", "maker": "lmu", "family": "lmu/garfield", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6606, "dossier_id": "lmu/glotlid/glotlid", "name": "GlotLID", "maker": "lmu", "family": "lmu/glotlid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6607, "dossier_id": "lmu/ldm/ldm-celebahq-256", "name": "LDM CelebA-HQ 256", "maker": "lmu", "family": "lmu/ldm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6608, "dossier_id": "lmu/ldm/ldm-super-resolution-4x-openimages", "name": "LDM Super-Resolution 4x OpenImages", "maker": "lmu", "family": "lmu/ldm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "4x super-resolution. The card loads `LDMSuperResolutionPipeline`, resizes an input to 128x128, and samples 100 steps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6609, "dossier_id": "lmu/mask/mask", "name": "MASK", "maker": "lmu", "family": "lmu/mask", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6610, "dossier_id": "lmu/myriad/myriad", "name": "MYRIAD", "maker": "lmu", "family": "lmu/myriad", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6611, "dossier_id": "lmu/rayder/rayder", "name": "RayDer", "maker": "lmu", "family": "lmu/rayder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6612, "dossier_id": "lmu/scflow/catfm", "name": "CAtFM", "maker": "lmu", "family": "lmu/scflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6613, "dossier_id": "lmu/scflow/scflow", "name": "SCFlow", "maker": "lmu", "family": "lmu/scflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6614, "dossier_id": "lmu/vicis/vicis", "name": "VICIS", "maker": "lmu", "family": "lmu/vicis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6615, "dossier_id": "lmu/what-moves/what-moves", "name": "WhatMoves", "maker": "lmu", "family": "lmu/what-moves", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6616, "dossier_id": "lmu/zipmo/zipmo", "name": "ZipMo", "maker": "lmu", "family": "lmu/zipmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6617, "dossier_id": "loughborough/fctsfn/fctsfn", "name": "FCTSFN", "maker": "loughborough", "family": "loughborough/fctsfn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6618, "dossier_id": "lsu/paine/naive-paine", "name": "Na\u00efve PAINE", "maker": "lsu", "family": "lsu/paine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6619, "dossier_id": "luma/genie/genie", "name": "Genie 1.0", "maker": "luma", "family": "luma/genie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6620, "dossier_id": "luma/photon/photon", "name": "Photon", "maker": "luma", "family": "luma/photon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.015, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6621, "dossier_id": "luma/photon/photon-flash", "name": "Photon Flash", "maker": "luma", "family": "luma/photon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.002, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6622, "dossier_id": "luma/ray/ray-1", "name": "Ray 1", "maker": "luma", "family": "luma/ray", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6623, "dossier_id": "luma/ray/ray-2", "name": "Ray 2", "maker": "luma", "family": "luma/ray", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6624, "dossier_id": "luma/ray/ray-2-flash", "name": "Ray 2 Flash", "maker": "luma", "family": "luma/ray", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6625, "dossier_id": "luma/ray/ray-3", "name": "Ray 3", "maker": "luma", "family": "luma/ray", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6626, "dossier_id": "luma/ray/ray-3-14", "name": "Ray 3.14", "maker": "luma", "family": "luma/ray", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6627, "dossier_id": "luma/ray/ray-3-2", "name": "Ray 3.2", "maker": "luma", "family": "luma/ray", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6628, "dossier_id": "luma/ray/ray-3-modify", "name": "Ray3 Modify", "maker": "luma", "family": "luma/ray", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6629, "dossier_id": "luma/uni/uni-1", "name": "Uni-1", "maker": "luma", "family": "luma/uni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.0404, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6630, "dossier_id": "luma/uni/uni-1-max", "name": "Uni-1 Max", "maker": "luma", "family": "luma/uni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.1, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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\"`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6631, "dossier_id": "magdeburg/dilithium/attwnet3d", "name": "AttWNet3D", "maker": "magdeburg", "family": "magdeburg/dilithium", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6632, "dossier_id": "magdeburg/dilithium/wnet-mss3d", "name": "WNet-MSS3D", "maker": "magdeburg", "family": "magdeburg/dilithium", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6633, "dossier_id": "magdeburg/ds6/ds6", "name": "DS6", "maker": "magdeburg", "family": "magdeburg/ds6", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6634, "dossier_id": "magdeburg/ftsrdyn/ftsrdyn", "name": "FTSRDyn", "maker": "magdeburg", "family": "magdeburg/ftsrdyn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6635, "dossier_id": "magdeburg/gp/gpreconresnet", "name": "GPReconResNet", "maker": "magdeburg", "family": "magdeburg/gp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6636, "dossier_id": "magdeburg/gp/gpshuffleunet", "name": "GPShuffleUNet", "maker": "magdeburg", "family": "magdeburg/gp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6637, "dossier_id": "magdeburg/gp/gpunet", "name": "GPUNet", "maker": "magdeburg", "family": "magdeburg/gp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6638, "dossier_id": "magdeburg/ncc1701/reconresnet", "name": "ReconResNet", "maker": "magdeburg", "family": "magdeburg/ncc1701", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6639, "dossier_id": "magdeburg/pulaski/pulaski", "name": "PULASki", "maker": "magdeburg", "family": "magdeburg/pulaski", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6640, "dossier_id": "magdeburg/spockmip/spockmip", "name": "SPOCKMIP", "maker": "magdeburg", "family": "magdeburg/spockmip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6641, "dossier_id": "magdeburg/strega/ccevae", "name": "cceVAE", "maker": "magdeburg", "family": "magdeburg/strega", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6642, "dossier_id": "mannheim/babelbert/babelbert-xlm-r", "name": "BabelBERT XLM-R", "maker": "mannheim", "family": "mannheim/babelbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6643, "dossier_id": "mannheim/rdf2vec/rdf2vec", "name": "RDF2vec", "maker": "mannheim", "family": "mannheim/rdf2vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6644, "dossier_id": "mannheim/tesseract/frak2021", "name": "frak2021", "maker": "mannheim", "family": "mannheim/tesseract", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6645, "dossier_id": "mannheim/tesseract/fraktur-5000000", "name": "Fraktur_5000000", "maker": "mannheim", "family": "mannheim/tesseract", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6646, "dossier_id": "mannheim/tesseract/german-print", "name": "german_print", "maker": "mannheim", "family": "mannheim/tesseract", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6647, "dossier_id": "mannheim/tesseract/gt4histocr", "name": "GT4HistOCR", "maker": "mannheim", "family": "mannheim/tesseract", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6648, "dossier_id": "mannheim/tod/tod-xlmr", "name": "TOD-XLMR", "maker": "mannheim", "family": "mannheim/tod", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6649, "dossier_id": "mbzuai/ain/ain", "name": "AIN", "maker": "mbzuai", "family": "mbzuai/ain", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language model for English and Arabic. The expanded name was not on the captured README.", "summary_intro": "AIN is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6650, "dossier_id": "mbzuai/amber/amber", "name": "Amber", "maker": "mbzuai", "family": "mbzuai/amber", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6651, "dossier_id": "mbzuai/araseg/araseg", "name": "araseg", "maker": "mbzuai", "family": "mbzuai/araseg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not verified beyond the repository name.", "summary_intro": "araseg is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6652, "dossier_id": "mbzuai/artst/artst", "name": "ArTST", "maker": "mbzuai", "family": "mbzuai/artst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic speech recognition and, in the ClArTTS tune, speech synthesis.", "summary_intro": "ArTST is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6653, "dossier_id": "mbzuai/bactrian-x/bactrian-x-bloom-7b1", "name": "Bactrian-X BLOOM 7.1B", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual instruction adapter for BLOOM-7B1.", "summary_intro": "Bactrian-X BLOOM 7.1B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6654, "dossier_id": "mbzuai/bactrian-x/bactrian-x-llama-13b", "name": "Bactrian-X LLaMA 13B", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual instruction following at 13B.", "summary_intro": "Bactrian-X LLaMA 13B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6655, "dossier_id": "mbzuai/bactrian-x/bactrian-x-llama-7b", "name": "Bactrian-X LLaMA 7B", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual instruction following. The card names 52 languages and those two instruction sets.", "summary_intro": "Bactrian-X LLaMA 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6656, "dossier_id": "mbzuai/bactrian-x/bactrian-x-mt5-large", "name": "Bactrian-X mT5-large", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual instruction adapter.", "summary_intro": "Bactrian-X mT5-large is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6657, "dossier_id": "mbzuai/bactrian-x/bactrian-x-mt5-xl", "name": "Bactrian-X mT5-xl", "maker": "mbzuai", "family": "mbzuai/bactrian-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual instruction adapter.", "summary_intro": "Bactrian-X mT5-xl is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6658, "dossier_id": "mbzuai/bimedix/bimedix", "name": "BiMediX", "maker": "mbzuai", "family": "mbzuai/bimedix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Medical question answering and chat in English, Arabic, or both. Not a device for unattended clinical decisions.", "summary_intro": "BiMediX is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6659, "dossier_id": "mbzuai/bimedix2/bimedix2-4b", "name": "BiMediX2 4B", "maker": "mbzuai", "family": "mbzuai/bimedix2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomedical image and text question answering. The 8B card title calls it a bio-medical expert LMM.", "summary_intro": "BiMediX2 4B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6660, "dossier_id": "mbzuai/bimedix2/bimedix2-70b", "name": "BiMediX2 70B", "maker": "mbzuai", "family": "mbzuai/bimedix2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomedical image and text question answering. The 8B card title calls it a bio-medical expert LMM.", "summary_intro": "BiMediX2 70B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6661, "dossier_id": "mbzuai/bimedix2/bimedix2-8b", "name": "BiMediX2 8B", "maker": "mbzuai", "family": "mbzuai/bimedix2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomedical image and text question answering. The 8B card title calls it a bio-medical expert LMM.", "summary_intro": "BiMediX2 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6662, "dossier_id": "mbzuai/bimedix2/bimedix2-8b-bi", "name": "BiMediX2 8B bilingual", "maker": "mbzuai", "family": "mbzuai/bimedix2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomedical image and text question answering. The 8B card title calls it a bio-medical expert LMM.", "summary_intro": "BiMediX2 8B bilingual is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6663, "dossier_id": "mbzuai/bimedix3/bimedix3-70b", "name": "BiMediX3 70B", "maker": "mbzuai", "family": "mbzuai/bimedix3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not verified beyond the repository name.", "summary_intro": "BiMediX3 70B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6664, "dossier_id": "mbzuai/come-vl/come-vl", "name": "CoME-VL", "maker": "mbzuai", "family": "mbzuai/come-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Visual understanding and grounding.", "summary_intro": "CoME-VL is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6665, "dossier_id": "mbzuai/crystal/crystal", "name": "Crystal", "maker": "mbzuai", "family": "mbzuai/crystal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6666, "dossier_id": "mbzuai/crystal/crystalchat-7b-web2code", "name": "CrystalChat-7B-Web2Code", "maker": "mbzuai", "family": "mbzuai/crystal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6667, "dossier_id": "mbzuai/doatlas/doatlas-2", "name": "DoAtlas-2", "maker": "mbzuai", "family": "mbzuai/doatlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The report says it runs a research cycle on human-data questions, from forming a question to testing a hypothesis.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6668, "dossier_id": "mbzuai/geochat/geochat-7b", "name": "GeoChat 7B", "maker": "mbzuai", "family": "mbzuai/geochat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Remote-sensing scene interpretation and grounded visual conversation.", "summary_intro": "GeoChat 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6669, "dossier_id": "mbzuai/geopixel/geopixel-7b", "name": "GeoPixel 7B", "maker": "mbzuai", "family": "mbzuai/geopixel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Grounded conversation on remote-sensing images.", "summary_intro": "GeoPixel 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6670, "dossier_id": "mbzuai/glamm/glamm", "name": "GLaMM", "maker": "mbzuai", "family": "mbzuai/glamm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Grounded vision-language conversation and region-level tasks.", "summary_intro": "GLaMM is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6671, "dossier_id": "mbzuai/guru/guru-32b", "name": "Guru-32B", "maker": "mbzuai", "family": "mbzuai/guru", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Guru-32B is an IFM reasoning checkpoint associated with the Guru / Reasoning360 paper. Hub date 2025-05-15. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6672, "dossier_id": "mbzuai/guru/guru-7b", "name": "Guru-7B", "maker": "mbzuai", "family": "mbzuai/guru", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Guru-7B is an IFM reasoning checkpoint associated with the Guru / Reasoning360 paper. Hub date 2025-05-14. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6673, "dossier_id": "mbzuai/jais/jais-13b", "name": "Jais 13B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6674, "dossier_id": "mbzuai/jais/jais-30b", "name": "Jais 30B", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6675, "dossier_id": "mbzuai/jais/jais-family-256m", "name": "Jais Family 256M", "maker": "mbzuai", "family": "mbzuai/jais", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6676, "dossier_id": "mbzuai/jais-asr/jais-asr", "name": "Jais-ASR", "maker": "mbzuai", "family": "mbzuai/jais-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6677, "dossier_id": "mbzuai/k2/k2", "name": "K2", "maker": "mbzuai", "family": "mbzuai/k2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English text generation with public training artifacts, plus a chat tune.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6678, "dossier_id": "mbzuai/k2-think/k2-think", "name": "K2-Think", "maker": "mbzuai", "family": "mbzuai/k2-think", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6679, "dossier_id": "mbzuai/k2-think/k2-think-v2", "name": "K2 Think V2", "maker": "mbzuai", "family": "mbzuai/k2-think", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6680, "dossier_id": "mbzuai/lamini/lamini-cerebras-1-3b", "name": "LaMini-Cerebras-1.3B", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Cerebras-1.3B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6681, "dossier_id": "mbzuai/lamini/lamini-cerebras-111m", "name": "LaMini-Cerebras-111M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Cerebras-111M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6682, "dossier_id": "mbzuai/lamini/lamini-cerebras-256m", "name": "LaMini-Cerebras-256M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Cerebras-256M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6683, "dossier_id": "mbzuai/lamini/lamini-cerebras-590m", "name": "LaMini-Cerebras-590M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Cerebras-590M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6684, "dossier_id": "mbzuai/lamini/lamini-flan-t5-248m", "name": "LaMini-Flan-T5-248M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Flan-T5-248M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6685, "dossier_id": "mbzuai/lamini/lamini-flan-t5-77m", "name": "LaMini-Flan-T5-77M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Flan-T5-77M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6686, "dossier_id": "mbzuai/lamini/lamini-flan-t5-783m", "name": "LaMini-Flan-T5-783M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Flan-T5-783M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6687, "dossier_id": "mbzuai/lamini/lamini-gpt-1-5b", "name": "LaMini-GPT-1.5B", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-GPT-1.5B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6688, "dossier_id": "mbzuai/lamini/lamini-gpt-124m", "name": "LaMini-GPT-124M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-GPT-124M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6689, "dossier_id": "mbzuai/lamini/lamini-gpt-774m", "name": "LaMini-GPT-774M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-GPT-774M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6690, "dossier_id": "mbzuai/lamini/lamini-neo-1-3b", "name": "LaMini-Neo-1.3B", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Neo-1.3B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6691, "dossier_id": "mbzuai/lamini/lamini-neo-125m", "name": "LaMini-Neo-125M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-Neo-125M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6692, "dossier_id": "mbzuai/lamini/lamini-t5-223m", "name": "LaMini-T5-223M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-T5-223M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6693, "dossier_id": "mbzuai/lamini/lamini-t5-61m", "name": "LaMini-T5-61M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-T5-61M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6694, "dossier_id": "mbzuai/lamini/lamini-t5-738m", "name": "LaMini-T5-738M", "maker": "mbzuai", "family": "mbzuai/lamini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says this checkpoint belongs to the LaMini-LM series of models distilled from large-scale instructions.", "summary_intro": "LaMini-T5-738M is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6695, "dossier_id": "mbzuai/llava/llava-llama-3-8b", "name": "LLaVA Llama-3 8B", "maker": "mbzuai", "family": "mbzuai/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language understanding.", "summary_intro": "LLaVA Llama-3 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6696, "dossier_id": "mbzuai/llava/llava-phi-3-mini", "name": "LLaVA Phi-3-mini", "maker": "mbzuai", "family": "mbzuai/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language understanding with a small Phi-3 language model.", "summary_intro": "LLaVA Phi-3-mini is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6697, "dossier_id": "mbzuai/llmvox/llmvox", "name": "LLMVoX", "maker": "mbzuai", "family": "mbzuai/llmvox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming speech for text produced by an LLM.", "summary_intro": "LLMVoX is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6698, "dossier_id": "mbzuai/medix-r1/medix-r1-2b", "name": "MediX-R1 2B", "maker": "mbzuai", "family": "mbzuai/medix-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-ended medical image and text reasoning. Not a substitute for a clinician.", "summary_intro": "MediX-R1 2B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6699, "dossier_id": "mbzuai/medix-r1/medix-r1-30b", "name": "MediX-R1 30B", "maker": "mbzuai", "family": "mbzuai/medix-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-ended medical image and text reasoning. Not a substitute for a clinician.", "summary_intro": "MediX-R1 30B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6700, "dossier_id": "mbzuai/medix-r1/medix-r1-8b", "name": "MediX-R1 8B", "maker": "mbzuai", "family": "mbzuai/medix-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-ended medical image and text reasoning. Not a substitute for a clinician.", "summary_intro": "MediX-R1 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6701, "dossier_id": "mbzuai/medmo/medmo-4b", "name": "MedMO 4B", "maker": "mbzuai", "family": "mbzuai/medmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Grounding and understanding medical images, as the README title states.", "summary_intro": "MedMO 4B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6702, "dossier_id": "mbzuai/medmo/medmo-8b", "name": "MedMO 8B", "maker": "mbzuai", "family": "mbzuai/medmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Grounding and understanding medical images, as the README title states.", "summary_intro": "MedMO 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6703, "dossier_id": "mbzuai/megamath/megamath-llama-3-2-1b", "name": "MegaMath-Llama-3.2-1B", "maker": "mbzuai", "family": "mbzuai/megamath", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 1B card says the model is for math problems using either chain-of-thought or program-aided language.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6704, "dossier_id": "mbzuai/megamath/megamath-llama-3-2-3b", "name": "MegaMath-Llama-3.2-3B", "maker": "mbzuai", "family": "mbzuai/megamath", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 1B card says the model is for math problems using either chain-of-thought or program-aided language.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6705, "dossier_id": "mbzuai/mobillama/mobillama-0-5b", "name": "MobiLlama 0.5B", "maker": "mbzuai", "family": "mbzuai/mobillama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device English text generation.", "summary_intro": "MobiLlama 0.5B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6706, "dossier_id": "mbzuai/mobillama/mobillama-0-8b", "name": "MobiLlama 0.8B", "maker": "mbzuai", "family": "mbzuai/mobillama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device English text generation.", "summary_intro": "MobiLlama 0.8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6707, "dossier_id": "mbzuai/mobillama/mobillama-1b", "name": "MobiLlama 1B", "maker": "mbzuai", "family": "mbzuai/mobillama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device English text generation. The card argues against using only the largest models when memory and energy matter.", "summary_intro": "MobiLlama 1B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6708, "dossier_id": "mbzuai/nanda/nanda-10b", "name": "Nanda 10B", "maker": "mbzuai", "family": "mbzuai/nanda", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6709, "dossier_id": "mbzuai/nanda/nanda-87b", "name": "Nanda 87B", "maker": "mbzuai", "family": "mbzuai/nanda", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not verified beyond the repository name and a Hub activity line that called it a distilled model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6710, "dossier_id": "mbzuai/omni-embed/omni-embed-mini-0-9b", "name": "Omni-Embed-Mini 0.9B", "maker": "mbzuai", "family": "mbzuai/omni-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings. The collection name is Omni. The exact task mix was not read from the card.", "summary_intro": "Omni-Embed-Mini 0.9B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6711, "dossier_id": "mbzuai/omni-embed/omni-embed-mini-2-3b", "name": "Omni-Embed-Mini 2.3B", "maker": "mbzuai", "family": "mbzuai/omni-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings. The collection name is Omni. The exact task mix was not read from the card.", "summary_intro": "Omni-Embed-Mini 2.3B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6712, "dossier_id": "mbzuai/openearthagent/openearthagent", "name": "OpenEarthAgent", "maker": "mbzuai", "family": "mbzuai/openearthagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Remote-sensing analysis with natural-language instructions.", "summary_intro": "OpenEarthAgent is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6713, "dossier_id": "mbzuai/palo/mobilepalo-1-7b", "name": "MobilePALO 1.7B", "maker": "mbzuai", "family": "mbzuai/palo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language generation. A fuller purpose statement was not captured from the README.", "summary_intro": "MobilePALO 1.7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6714, "dossier_id": "mbzuai/palo/palo-13b", "name": "PALO 13B", "maker": "mbzuai", "family": "mbzuai/palo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language generation. A fuller purpose statement was not captured from the README.", "summary_intro": "PALO 13B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6715, "dossier_id": "mbzuai/palo/palo-7b", "name": "PALO 7B", "maker": "mbzuai", "family": "mbzuai/palo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language generation. A fuller purpose statement was not captured from the README.", "summary_intro": "PALO 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6716, "dossier_id": "mbzuai/pan/pan", "name": "PAN", "maker": "mbzuai", "family": "mbzuai/pan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper describes a general world model for action-conditioned simulation, long-horizon forecasting, and planning, using natural-language actions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6717, "dossier_id": "mbzuai/swiftformer/swiftformer-l1", "name": "SwiftFormer-L1", "maker": "mbzuai", "family": "mbzuai/swiftformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time mobile image classification, as stated by the SwiftFormer paper linked from the XS card.", "summary_intro": "SwiftFormer-L1 is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6718, "dossier_id": "mbzuai/swiftformer/swiftformer-l3", "name": "SwiftFormer-L3", "maker": "mbzuai", "family": "mbzuai/swiftformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time mobile image classification, as stated by the SwiftFormer paper linked from the XS card.", "summary_intro": "SwiftFormer-L3 is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6719, "dossier_id": "mbzuai/swiftformer/swiftformer-s", "name": "SwiftFormer-S", "maker": "mbzuai", "family": "mbzuai/swiftformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time mobile image classification, as stated by the SwiftFormer paper linked from the XS card.", "summary_intro": "SwiftFormer-S is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6720, "dossier_id": "mbzuai/swiftformer/swiftformer-xs", "name": "SwiftFormer-XS", "maker": "mbzuai", "family": "mbzuai/swiftformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time mobile image classification, as stated by the SwiftFormer paper linked from the XS card.", "summary_intro": "SwiftFormer-XS is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6721, "dossier_id": "mbzuai/terrafm/terrafm", "name": "TerraFM", "maker": "mbzuai", "family": "mbzuai/terrafm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earth-observation representation learning across sensors.", "summary_intro": "TerraFM is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6722, "dossier_id": "mbzuai/video-chatgpt/video-chatgpt-7b", "name": "Video-ChatGPT 7B", "maker": "mbzuai", "family": "mbzuai/video-chatgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Conversation about videos.", "summary_intro": "Video-ChatGPT 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6723, "dossier_id": "mbzuai/video-com/video-com", "name": "Video-CoM", "maker": "mbzuai", "family": "mbzuai/video-com", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not verified beyond the repository name and the Apache 2.0 metadata.", "summary_intro": "Video-CoM is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6724, "dossier_id": "mbzuai/video-r2/video-r2", "name": "Video-R2", "maker": "mbzuai", "family": "mbzuai/video-r2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Not verified beyond the repository name and the Apache 2.0 metadata.", "summary_intro": "Video-R2 is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6725, "dossier_id": "mbzuai/videogpt-plus/videogpt-plus-llama3-8b", "name": "VideoGPT+ Llama-3 8B", "maker": "mbzuai", "family": "mbzuai/videogpt-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video understanding.", "summary_intro": "VideoGPT+ Llama-3 8B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6726, "dossier_id": "mbzuai/videogpt-plus/videogpt-plus-phi3", "name": "VideoGPT+ Phi-3-mini", "maker": "mbzuai", "family": "mbzuai/videogpt-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video question answering and caption benchmarks.", "summary_intro": "VideoGPT+ Phi-3-mini is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6727, "dossier_id": "mbzuai/videogpt-plus/videogpt-plus-vicuna-13b", "name": "VideoGPT+ Vicuna 13B", "maker": "mbzuai", "family": "mbzuai/videogpt-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video understanding.", "summary_intro": "VideoGPT+ Vicuna 13B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6728, "dossier_id": "mbzuai/videogpt-plus/videogpt-plus-vicuna-7b", "name": "VideoGPT+ Vicuna 7B", "maker": "mbzuai", "family": "mbzuai/videogpt-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video understanding.", "summary_intro": "VideoGPT+ Vicuna 7B is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6729, "dossier_id": "mbzuai/vimul/vimul", "name": "ViMUL", "maker": "mbzuai", "family": "mbzuai/vimul", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual video understanding.", "summary_intro": "ViMUL is a named MBZUAI release. Collected 2026-10-15. Status: active.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6730, "dossier_id": "mbzuai/worldguide/worldguide", "name": "WorldGuide", "maker": "mbzuai", "family": "mbzuai/worldguide", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "WorldGuide is MBZUAI's goal-directed video world model. A checkpoint was posted on 2 October 2026. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6731, "dossier_id": "mcmaster/eskape/eskape", "name": "ESKAPE Model", "maker": "mcmaster", "family": "mcmaster/eskape", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6732, "dossier_id": "mcmaster/imitsat/imitsat", "name": "ImitSAT", "maker": "mcmaster", "family": "mcmaster/imitsat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6733, "dossier_id": "mcmaster/synthemol/synthemol-mcts", "name": "SyntheMol-MCTS", "maker": "mcmaster", "family": "mcmaster/synthemol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6734, "dossier_id": "mcmaster/synthemol/synthemol-rl", "name": "SyntheMol-RL", "maker": "mcmaster", "family": "mcmaster/synthemol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6735, "dossier_id": "mcmaster/trajloom/trajloom", "name": "TrajLoom", "maker": "mcmaster", "family": "mcmaster/trajloom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6736, "dossier_id": "meituan/audio/longcat-audio-codec", "name": "LongCat-Audio-Codec", "maker": "meituan", "family": "meituan/audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6737, "dossier_id": "meituan/audio/longcat-audiodit-1b", "name": "LongCat-AudioDiT-1B", "maker": "meituan", "family": "meituan/audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6738, "dossier_id": "meituan/audio/longcat-audiodit-3-5b", "name": "LongCat-AudioDiT-3.5B", "maker": "meituan", "family": "meituan/audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6739, "dossier_id": "meituan/evocua/evocua-32b", "name": "EvoCUA-32B", "maker": "meituan", "family": "meituan/evocua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6740, "dossier_id": "meituan/evocua/evocua-8b", "name": "EvoCUA-8B", "maker": "meituan", "family": "meituan/evocua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6741, "dossier_id": "meituan/flash/longcat-flash-chat-2602-exp", "name": "LongCat-Flash-Chat-2602-Exp", "maker": "meituan", "family": "meituan/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6742, "dossier_id": "meituan/flash/longcat-flash-omni", "name": "LongCat-Flash-Omni", "maker": "meituan", "family": "meituan/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6743, "dossier_id": "meituan/flash/longcat-flash-omni-2603", "name": "LongCat-Flash-Omni-2603", "maker": "meituan", "family": "meituan/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6744, "dossier_id": "meituan/flash/longcat-flash-prover", "name": "LongCat-Flash-Prover", "maker": "meituan", "family": "meituan/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6745, "dossier_id": "meituan/flash/longcat-flash-thinking", "name": "LongCat-Flash-Thinking", "maker": "meituan", "family": "meituan/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6746, "dossier_id": "meituan/flash/longcat-flash-thinking-2601", "name": "LongCat-Flash-Thinking-2601", "maker": "meituan", "family": "meituan/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6747, "dossier_id": "meituan/flash/longcat-heavy-mode-summary", "name": "LongCat-HeavyMode-Summary", "maker": "meituan", "family": "meituan/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6748, "dossier_id": "meituan/image/longcat-image", "name": "LongCat-Image", "maker": "meituan", "family": "meituan/image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6749, "dossier_id": "meituan/image/longcat-image-dev", "name": "LongCat-Image-Dev", "maker": "meituan", "family": "meituan/image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6750, "dossier_id": "meituan/image/longcat-image-edit", "name": "LongCat-Image-Edit", "maker": "meituan", "family": "meituan/image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6751, "dossier_id": "meituan/image/longcat-image-edit-turbo", "name": "LongCat-Image-Edit-Turbo", "maker": "meituan", "family": "meituan/image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6752, "dossier_id": "meituan/longcat/longcat-deepresearch", "name": "LongCat-DeepResearch", "maker": "meituan", "family": "meituan/longcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6753, "dossier_id": "meituan/memocr/memocr-7b", "name": "MemOCR-7B", "maker": "meituan", "family": "meituan/memocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6754, "dossier_id": "meituan/next/longcat-next", "name": "LongCat-Next", "maker": "meituan", "family": "meituan/next", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6755, "dossier_id": "meituan/video/longcat-video", "name": "LongCat-Video", "maker": "meituan", "family": "meituan/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6756, "dossier_id": "meituan/video/longcat-video-avatar", "name": "LongCat-Video-Avatar", "maker": "meituan", "family": "meituan/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6757, "dossier_id": "meituan/video/longcat-video-avatar-1-5", "name": "LongCat-Video-Avatar-1.5", "maker": "meituan", "family": "meituan/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6758, "dossier_id": "meizu/flyme/flyme-ai", "name": "Flyme AI \u5927\u6a21\u578b", "maker": "meizu", "family": "meizu/flyme", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6759, "dossier_id": "melbourne/barbet/barbet-archaea-base", "name": "Barbet archaea base", "maker": "melbourne", "family": "melbourne/barbet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Taxonomic classification of a prokaryotic genome assembly. PyPI summary: Bacteria and Archaea Representation using Bidirectional Embeddings for Taxonomic classification.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6760, "dossier_id": "melbourne/barbet/barbet-archaea-large", "name": "Barbet archaea large", "maker": "melbourne", "family": "melbourne/barbet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Taxonomic classification of a prokaryotic genome assembly. PyPI summary: Bacteria and Archaea Representation using Bidirectional Embeddings for Taxonomic classification.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6761, "dossier_id": "melbourne/barbet/barbet-bacteria-base", "name": "Barbet bacteria base", "maker": "melbourne", "family": "melbourne/barbet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Taxonomic classification of a prokaryotic genome assembly. PyPI summary: Bacteria and Archaea Representation using Bidirectional Embeddings for Taxonomic classification.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6762, "dossier_id": "melbourne/barbet/barbet-bacteria-large", "name": "Barbet bacteria large", "maker": "melbourne", "family": "melbourne/barbet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Taxonomic classification of a prokaryotic genome assembly. PyPI summary: Bacteria and Archaea Representation using Bidirectional Embeddings for Taxonomic classification.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6763, "dossier_id": "melbourne/bixt/bixt-ti-16", "name": "BiXT-Ti/16", "maker": "melbourne", "family": "melbourne/bixt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6764, "dossier_id": "melbourne/bixt/bixt-ti-16-d13", "name": "BiXT-Ti/16 (d13)", "maker": "melbourne", "family": "melbourne/bixt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6765, "dossier_id": "melbourne/bixt/bixt-ti-4", "name": "BiXT-Ti/4", "maker": "melbourne", "family": "melbourne/bixt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6766, "dossier_id": "melbourne/bixt/bixt-ti-4-d13", "name": "BiXT-Ti/4 (d13)", "maker": "melbourne", "family": "melbourne/bixt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6767, "dossier_id": "melbourne/bixt/bixt-ti-8", "name": "BiXT-Ti/8", "maker": "melbourne", "family": "melbourne/bixt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6768, "dossier_id": "melbourne/bixt/bixt-ti-8-d13", "name": "BiXT-Ti/8 (d13)", "maker": "melbourne", "family": "melbourne/bixt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "ImageNet classification with a bi-directional cross-attention transformer. The weights page is the checkpoint card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6769, "dossier_id": "melbourne/corgi/corgi", "name": "Corgi", "maker": "melbourne", "family": "melbourne/corgi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6770, "dossier_id": "melbourne/terrier/terrier", "name": "Terrier", "maker": "melbourne", "family": "melbourne/terrier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6771, "dossier_id": "meshy/meshy/meshy-1", "name": "Meshy 1", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6772, "dossier_id": "meshy/meshy/meshy-2", "name": "Meshy 2", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6773, "dossier_id": "meshy/meshy/meshy-2-5", "name": "Meshy 2.5", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6774, "dossier_id": "meshy/meshy/meshy-3", "name": "Meshy 3", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6775, "dossier_id": "meshy/meshy/meshy-3-turbo", "name": "Meshy 3 Turbo", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6776, "dossier_id": "meshy/meshy/meshy-4", "name": "Meshy 4", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6777, "dossier_id": "meshy/meshy/meshy-5", "name": "Meshy 5", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6778, "dossier_id": "meshy/meshy/meshy-6", "name": "Meshy 6", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6779, "dossier_id": "meshy/meshy/meshy-6-lite", "name": "Meshy 6 Lite", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6780, "dossier_id": "meshy/meshy/meshy-7", "name": "Meshy 7", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6781, "dossier_id": "meshy/meshy/meshy-7-1", "name": "Meshy 7.1", "maker": "meshy", "family": "meshy/meshy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6782, "dossier_id": "meshy/smart-topology/meshy-t1", "name": "Meshy T1", "maker": "meshy", "family": "meshy/smart-topology", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6783, "dossier_id": "meshy/smart-topology/meshy-t2", "name": "Meshy T2", "maker": "meshy", "family": "meshy/smart-topology", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6784, "dossier_id": "meta/audiogen/audiogen-medium", "name": "AudioGen Medium", "maker": "meta", "family": "meta/audiogen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6785, "dossier_id": "meta/code-llama/code-llama-70b", "name": "Code Llama 70B", "maker": "meta", "family": "meta/code-llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6786, "dossier_id": "meta/llama/llama-13b", "name": "LLaMA 13B", "maker": "meta", "family": "meta/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6787, "dossier_id": "meta/llama/llama-33b", "name": "LLaMA 33B", "maker": "meta", "family": "meta/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6788, "dossier_id": "meta/llama/llama-4-behemoth", "name": "Llama 4 Behemoth", "maker": "meta", "family": "meta/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6789, "dossier_id": "meta/llama/llama-65b", "name": "LLaMA 65B", "maker": "meta", "family": "meta/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6790, "dossier_id": "meta/llama/llama-7b", "name": "LLaMA 7B", "maker": "meta", "family": "meta/llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6791, "dossier_id": "meta/llama-guard/llama-guard-2-8b", "name": "Llama Guard 2 8B", "maker": "meta", "family": "meta/llama-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classify prompts and responses as safe or unsafe and name the hazard categories.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6792, "dossier_id": "meta/llama-guard/llama-guard-3-11b-vision", "name": "Llama Guard 3 11B Vision", "maker": "meta", "family": "meta/llama-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6793, "dossier_id": "meta/llama-guard/llama-guard-3-1b", "name": "Llama Guard 3 1B", "maker": "meta", "family": "meta/llama-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6794, "dossier_id": "meta/llama-guard/llama-guard-3-8b", "name": "Llama Guard 3 8B", "maker": "meta", "family": "meta/llama-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Ship with Llama 3.1 as the text safeguard for prompts and model responses.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6795, "dossier_id": "meta/llama-guard/llama-guard-7b", "name": "Llama Guard 7B", "maker": "meta", "family": "meta/llama-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classify LLM inputs and outputs against a hazard taxonomy and return the violated categories.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6796, "dossier_id": "meta/muse/muse-image", "name": "Muse Image", "maker": "meta", "family": "meta/muse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6797, "dossier_id": "meta/muse/muse-voice-transcribe", "name": "Muse Voice Transcribe", "maker": "meta", "family": "meta/muse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6798, "dossier_id": "meta/musicgen/musicgen-large", "name": "MusicGen Large", "maker": "meta", "family": "meta/musicgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6799, "dossier_id": "meta/musicgen/musicgen-medium", "name": "MusicGen Medium", "maker": "meta", "family": "meta/musicgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6800, "dossier_id": "meta/musicgen/musicgen-melody", "name": "MusicGen Melody", "maker": "meta", "family": "meta/musicgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6801, "dossier_id": "meta/musicgen/musicgen-small", "name": "MusicGen Small", "maker": "meta", "family": "meta/musicgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6802, "dossier_id": "meta/prompt-guard/llama-prompt-guard-2-22m", "name": "Llama Prompt Guard 2 22M", "maker": "meta", "family": "meta/prompt-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classify text for prompt injection and jailbreak risk across the languages tagged on the model repo.", "summary_intro": "Llama Prompt Guard 2 22M is Meta's April 2025 prompt-attack classifier paired with Llama 4. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6803, "dossier_id": "meta/prompt-guard/llama-prompt-guard-2-86m", "name": "Llama Prompt Guard 2 86M", "maker": "meta", "family": "meta/prompt-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classify text for prompt injection and jailbreak risk across the languages tagged on the model repo.", "summary_intro": "Llama Prompt Guard 2 86M is Meta's April 2025 prompt-attack classifier paired with Llama 4. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6804, "dossier_id": "meta/prompt-guard/prompt-guard-86m", "name": "Prompt Guard 86M", "maker": "meta", "family": "meta/prompt-guard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6805, "dossier_id": "meta/sam/sam", "name": "Segment Anything Model", "maker": "meta", "family": "meta/sam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Return a mask for an object indicated by a visual prompt on a single image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6806, "dossier_id": "meta/sam/sam-2", "name": "SAM 2", "maker": "meta", "family": "meta/sam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6807, "dossier_id": "meta/sam/sam-2-1", "name": "SAM 2.1", "maker": "meta", "family": "meta/sam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same promptable image and video segmentation task as SAM 2, with the newer checkpoints and released training code.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6808, "dossier_id": "meta/sam/sam-3", "name": "SAM 3", "maker": "meta", "family": "meta/sam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6809, "dossier_id": "meta/sam/sam-3-1", "name": "SAM 3.1", "maker": "meta", "family": "meta/sam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6810, "dossier_id": "microsoft/aion/aion-instruct", "name": "Aion Instruct", "maker": "microsoft", "family": "microsoft/aion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6811, "dossier_id": "microsoft/florence/florence-2", "name": "Florence-2", "maker": "microsoft", "family": "microsoft/florence", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Captioning, object detection, segmentation, and other vision-language tasks from short prompts, including zero-shot use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6812, "dossier_id": "microsoft/harrier/harrier-oss-v1", "name": "Harrier OSS v1", "maker": "microsoft", "family": "microsoft/harrier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings. Other Harrier sizes, if any, were not confirmed.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6813, "dossier_id": "microsoft/healthcare/cxrreportgen-premium", "name": "CxrReportGen Premium", "maker": "microsoft", "family": "microsoft/healthcare", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research drafting of grounded radiology findings, including localization of findings, not unattended diagnosis.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6814, "dossier_id": "microsoft/healthcare/medimageinsight-premium", "name": "MedImageInsight Premium", "maker": "microsoft", "family": "microsoft/healthcare", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research and application development for medical image embeddings, including outlier and drift checks if the app implements them.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6815, "dossier_id": "microsoft/mai/mai-1-preview", "name": "MAI-1-preview", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6816, "dossier_id": "microsoft/mai/mai-base-1", "name": "MAI-Base-1", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6817, "dossier_id": "microsoft/mai/mai-base-2", "name": "MAI-Base-2", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6818, "dossier_id": "microsoft/mai/mai-code-1-flash", "name": "MAI-Code-1-Flash", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Inference-efficient coding assistance inside Microsoft's developer tools.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6819, "dossier_id": "microsoft/mai/mai-cyber-1-flash", "name": "MAI-Cyber-1-Flash", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6820, "dossier_id": "microsoft/mai/mai-image-1", "name": "MAI-Image-1", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6821, "dossier_id": "microsoft/mai/mai-image-2", "name": "MAI-Image-2", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 33.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6822, "dossier_id": "microsoft/mai/mai-image-2-efficient", "name": "MAI-Image-2-Efficient", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6823, "dossier_id": "microsoft/mai/mai-transcribe-1", "name": "MAI-Transcribe-1", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": 0.36, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6824, "dossier_id": "microsoft/mai/mai-transcribe-1-5", "name": "MAI-Transcribe-1.5", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6825, "dossier_id": "microsoft/mai/mai-transcribe-2", "name": "MAI-Transcribe-2", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual speech-to-text, including a Voice Live pairing with chat models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6826, "dossier_id": "microsoft/mai/mai-transcribe-2-streaming", "name": "MAI-Transcribe-2-Streaming", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": 0.54, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-latency partial and final transcripts for dictation, subtitles, and voice agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6827, "dossier_id": "microsoft/mai/mai-voice-1", "name": "MAI-Voice-1", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": 22.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Natural speech generation, including custom voices, for Copilot audio features and Foundry apps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6828, "dossier_id": "microsoft/mai/mai-voice-2", "name": "MAI-Voice-2", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Expressive speech for long-form audio and voice agents, with consent checks on custom voices.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6829, "dossier_id": "microsoft/mai/mai-voice-2-1", "name": "MAI-Voice-2.1", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual speech generation for conversational agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6830, "dossier_id": "microsoft/mai/mai-voice-2-1-flash", "name": "MAI-Voice-2.1-Flash", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower-latency speech generation paired with MAI-Voice-2.1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6831, "dossier_id": "microsoft/mai/mai-voice-2-flash", "name": "MAI-Voice-2-Flash", "maker": "microsoft", "family": "microsoft/mai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Microsoft described it as the efficient package of MAI-Voice-2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6832, "dossier_id": "microsoft/phi/phi-1-5", "name": "Phi-1.5", "maker": "microsoft", "family": "microsoft/phi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6833, "dossier_id": "microsoft/phi/phi-silica", "name": "Phi Silica", "maker": "microsoft", "family": "microsoft/phi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Local text generation, summarization, rewriting, and short content generation inside Windows apps, without a cloud round trip.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6834, "dossier_id": "microsoft/trellis/trellis", "name": "TRELLIS", "maker": "microsoft", "family": "microsoft/trellis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate 3D assets from images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6835, "dossier_id": "microsoft/trellis/trellis-2", "name": "TRELLIS.2", "maker": "microsoft", "family": "microsoft/trellis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-3D generation, as labeled on the model repository.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6836, "dossier_id": "microsoft/vibevoice/vibevoice-0-5b-streaming", "name": "VibeVoice-0.5B-Streaming", "maker": "microsoft", "family": "microsoft/vibevoice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming text-to-speech in the VibeVoice family. The 1.5B card does not describe the streaming checkpoint's limits.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6837, "dossier_id": "microsoft/vibevoice/vibevoice-asr-bitnet", "name": "VibeVoice-ASR-BitNet", "maker": "microsoft", "family": "microsoft/vibevoice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device or CPU transcription when a GPU is not available.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6838, "dossier_id": "midjourney/edit/edit", "name": "Midjourney Edit", "maker": "midjourney", "family": "midjourney/edit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6839, "dossier_id": "midjourney/midjourney/hd", "name": "Midjourney HD", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An early alternate look: busier and more detailed than the numbered V1-V3 models, still low coherency.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6840, "dossier_id": "midjourney/midjourney/test", "name": "Midjourney Test", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Community test generations while Midjourney tried features that were not in the default model yet.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6841, "dossier_id": "midjourney/midjourney/testp", "name": "Midjourney Testp", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The same experimental role as --test. Midjourney published one shared description for both flags.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6842, "dossier_id": "midjourney/midjourney/v1", "name": "Midjourney V1", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6843, "dossier_id": "midjourney/midjourney/v2", "name": "Midjourney V2", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation with a more colorful painterly look than V1, still at low coherency according to Midjourney.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6844, "dossier_id": "midjourney/midjourney/v3", "name": "Midjourney V3", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text and image prompting for creative compositions. It is the last main model before the V4 rewrite.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6845, "dossier_id": "midjourney/midjourney/v4", "name": "Midjourney V4", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6846, "dossier_id": "midjourney/midjourney/v5", "name": "Midjourney V5", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Photographic text-to-image and image-prompt generations at 1024 px, with a weaker default style than V5.1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6847, "dossier_id": "midjourney/midjourney/v5-1", "name": "Midjourney V5.1", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Easier photographic and designed images from short prompts, plus repeating patterns with --tile.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6848, "dossier_id": "midjourney/midjourney/v5-2", "name": "Midjourney V5.2", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6849, "dossier_id": "midjourney/midjourney/v6", "name": "Midjourney V6", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General text-to-image and image-prompt work, including character reference, which V7 later replaced with Omni Reference.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6850, "dossier_id": "midjourney/midjourney/v6-1", "name": "Midjourney V6.1", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The production image model between V6 and V7. Later V7 editing tools still call back into V6.1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6851, "dossier_id": "midjourney/midjourney/v7", "name": "Midjourney V7", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6852, "dossier_id": "midjourney/midjourney/v8", "name": "Midjourney V8", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6853, "dossier_id": "midjourney/midjourney/v8-1", "name": "Midjourney V8.1", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6854, "dossier_id": "midjourney/midjourney/v8-2", "name": "Midjourney V8.2", "maker": "midjourney", "family": "midjourney/midjourney", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6855, "dossier_id": "midjourney/niji/niji-4", "name": "Niji 4", "maker": "midjourney", "family": "midjourney/niji", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6856, "dossier_id": "midjourney/niji/niji-5", "name": "Niji 5", "maker": "midjourney", "family": "midjourney/niji", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Anime and illustrated images, with named style presets for cute characters, expressive illustration, the original look, and scenic shots.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6857, "dossier_id": "midjourney/niji/niji-6", "name": "Niji 6", "maker": "midjourney", "family": "midjourney/niji", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Anime and Eastern illustration, plus character reference, which V7 and Niji 7 moved away from.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6858, "dossier_id": "midjourney/niji/niji-7", "name": "Niji 7", "maker": "midjourney", "family": "midjourney/niji", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6859, "dossier_id": "midjourney/video/video-v1", "name": "Midjourney Video V1", "maker": "midjourney", "family": "midjourney/video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6860, "dossier_id": "mila/focalcodec/focalcodec-12-5hz", "name": "FocalCodec 12.5 Hz", "maker": "mila", "family": "mila/focalcodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6861, "dossier_id": "mila/focalcodec/focalcodec-25hz", "name": "FocalCodec 25 Hz", "maker": "mila", "family": "mila/focalcodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6862, "dossier_id": "mila/focalcodec/focalcodec-50hz", "name": "FocalCodec 50 Hz", "maker": "mila", "family": "mila/focalcodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6863, "dossier_id": "mila/focalcodec/focalcodec-50hz-2k-causal", "name": "FocalCodec-Stream 50 Hz 2k", "maker": "mila", "family": "mila/focalcodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time speech coding with one causal codebook, for live tokenization rather than the offline FocalCodec checkpoints.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6864, "dossier_id": "mila/focalcodec/focalcodec-50hz-4k-causal", "name": "FocalCodec-Stream 50 Hz 4k", "maker": "mila", "family": "mila/focalcodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time single-codebook speech coding. The GitHub streaming example loads this config.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6865, "dossier_id": "mila/focalcodec/focalcodec-50hz-65k-causal", "name": "FocalCodec-Stream 50 Hz 65k", "maker": "mila", "family": "mila/focalcodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The high-codebook streaming codec in the FocalCodec-Stream trio, still one codebook, for real-time speech tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6866, "dossier_id": "mila/galileo/galileo-base", "name": "Galileo Base", "maker": "mila", "family": "mila/galileo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6867, "dossier_id": "mila/galileo/galileo-nano", "name": "Galileo Nano", "maker": "mila", "family": "mila/galileo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6868, "dossier_id": "mila/galileo/galileo-tiny", "name": "Galileo Tiny", "maker": "mila", "family": "mila/galileo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6869, "dossier_id": "mines/tempest/tempest-1", "name": "TempEst 1", "maker": "mines", "family": "mines/tempest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6870, "dossier_id": "mines/tempest/tempest-2", "name": "TempEst 2", "maker": "mines", "family": "mines/tempest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6871, "dossier_id": "mines/tempest/tempest-newt", "name": "TempEst-NEWT", "maker": "mines", "family": "mines/tempest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6872, "dossier_id": "mines/tempest/tempest-next", "name": "TempEst-NEXT", "maker": "mines", "family": "mines/tempest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6873, "dossier_id": "minho/deepmol/ames", "name": "AMES Mutagenicity", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6874, "dossier_id": "minho/deepmol/bbb", "name": "BBB (Blood-Brain Barrier)", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6875, "dossier_id": "minho/deepmol/bioavailability", "name": "Bioavailability", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6876, "dossier_id": "minho/deepmol/caco2", "name": "Caco-2 (Cell Effective Permeability)", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6877, "dossier_id": "minho/deepmol/cyp2c9-inhibition", "name": "CYP P450 2C9 Inhibition", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6878, "dossier_id": "minho/deepmol/cyp2c9-substrate", "name": "CYP2C9 Substrate", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6879, "dossier_id": "minho/deepmol/cyp2d6-substrate", "name": "CYP2D6 Substrate", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6880, "dossier_id": "minho/deepmol/cyp3a4-inhibition", "name": "CYP P450 3A4 Inhibition", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6881, "dossier_id": "minho/deepmol/cyp3a4-substrate", "name": "CYP3A4 Substrate", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6882, "dossier_id": "minho/deepmol/hepatocyte-clearance", "name": "Hepatocyte Clearance", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6883, "dossier_id": "minho/deepmol/herg-blockers", "name": "hERG blockers", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6884, "dossier_id": "minho/deepmol/hia", "name": "HIA (Human Intestinal Absorption)", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6885, "dossier_id": "minho/deepmol/ld50", "name": "Acute toxicity LD50", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6886, "dossier_id": "minho/deepmol/lipophilicity", "name": "Lipophilicity", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6887, "dossier_id": "minho/deepmol/microsome-clearance", "name": "Microsome Clearance", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6888, "dossier_id": "minho/deepmol/plants-sm-precursor", "name": "Plants secondary metabolite precursors predictor", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6889, "dossier_id": "minho/deepmol/ppbr", "name": "Human plasma protein binding rate (PPBR)", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6890, "dossier_id": "minho/deepmol/solubility", "name": "Solubility", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6891, "dossier_id": "minho/deepmol/vdss", "name": "Volume of Distribution at steady state", "maker": "minho", "family": "minho/deepmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6892, "dossier_id": "minimax/h3/minimax-h3", "name": "MiniMax-H3", "maker": "minimax", "family": "minimax/h3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General text-to-video, image-to-video, first-and-last-frame, and multimodal reference generation with native audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6893, "dossier_id": "minimax/h3/minimax-h3-max", "name": "MiniMax-H3-Max", "maker": "minimax", "family": "minimax/h3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster text-to-video, image-to-video, and reference video than H3, without 2K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6894, "dossier_id": "minimax/hailuo/i2v-01", "name": "I2V-01", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Animate a first-frame image with an optional prompt.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6895, "dossier_id": "minimax/hailuo/i2v-01-director", "name": "I2V-01-Director", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-video with explicit camera moves.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6896, "dossier_id": "minimax/hailuo/i2v-01-live", "name": "I2V-01-live", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-video. A distinct live or illustration behavior was not confirmed on the API page itself.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6897, "dossier_id": "minimax/hailuo/minimax-hailuo-02", "name": "MiniMax-Hailuo-02", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6898, "dossier_id": "minimax/hailuo/minimax-hailuo-2-3", "name": "MiniMax-Hailuo-2.3", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-video and image-to-video at 768P or 1080P.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6899, "dossier_id": "minimax/hailuo/minimax-hailuo-2-3-fast", "name": "MiniMax-Hailuo-2.3-Fast", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lower-cost image-to-video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6900, "dossier_id": "minimax/hailuo/s2v-01", "name": "S2V-01", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate a video that keeps a referenced character\u2019s face.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6901, "dossier_id": "minimax/hailuo/t2v-01", "name": "T2V-01", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate a short video from a text prompt.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6902, "dossier_id": "minimax/hailuo/t2v-01-director", "name": "T2V-01-Director", "maker": "minimax", "family": "minimax/hailuo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-video with cinematic camera instructions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6903, "dossier_id": "minimax/image/image-01", "name": "image-01", "maker": "minimax", "family": "minimax/image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image and image-to-image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6904, "dossier_id": "minimax/image/image-01-live", "name": "image-01-live", "maker": "minimax", "family": "minimax/image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Styled or reference image generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6905, "dossier_id": "minimax/music/music-1-5", "name": "Music-1.5", "maker": "minimax", "family": "minimax/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vocal music, with the public release aimed at listenable four-minute songs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6906, "dossier_id": "minimax/music/music-2-0", "name": "Music-2.0", "maker": "minimax", "family": "minimax/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vocal songs up to about five minutes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6907, "dossier_id": "minimax/music/music-2-5", "name": "Music-2.5", "maker": "minimax", "family": "minimax/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Detailed vocal songs from lyrics and a style prompt.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6908, "dossier_id": "minimax/music/music-2-5-plus", "name": "Music-2.5+", "maker": "minimax", "family": "minimax/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Songs and instrumentals from a prompt, breaking the vocal-only limit of earlier music models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6909, "dossier_id": "minimax/music/music-2-6", "name": "music-2.6", "maker": "minimax", "family": "minimax/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vocal music from a prompt and lyrics. The release title also points at cover and bass changes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6910, "dossier_id": "minimax/music/music-3-0", "name": "music-3.0", "maker": "minimax", "family": "minimax/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full songs from a style description and lyrics, up to five minutes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6911, "dossier_id": "minimax/music/music-cover", "name": "music-cover", "maker": "minimax", "family": "minimax/music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Music generation. A dedicated behavior spec was not on the pages read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6912, "dossier_id": "minimax/speech/speech-02-hd", "name": "speech-02-hd", "maker": "minimax", "family": "minimax/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6913, "dossier_id": "minimax/speech/speech-02-turbo", "name": "speech-02-turbo", "maker": "minimax", "family": "minimax/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6914, "dossier_id": "minimax/speech/speech-2-5", "name": "Speech-2.5", "maker": "minimax", "family": "minimax/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher voice similarity and wider language coverage than Speech-02, according to the release note.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6915, "dossier_id": "minimax/speech/speech-2-6-hd", "name": "speech-2.6-hd", "maker": "minimax", "family": "minimax/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6916, "dossier_id": "minimax/speech/speech-2-6-turbo", "name": "speech-2.6-turbo", "maker": "minimax", "family": "minimax/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6917, "dossier_id": "minimax/speech/speech-2-8-hd", "name": "speech-2.8-hd", "maker": "minimax", "family": "minimax/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6918, "dossier_id": "minimax/speech/speech-2-8-turbo", "name": "speech-2.8-turbo", "maker": "minimax", "family": "minimax/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech, including system voices and cloned voices, on the T2A HTTP and WebSocket APIs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6919, "dossier_id": "minimax/vtp/vtp-base-f16d64", "name": "VTP-Base-f16d64", "maker": "minimax", "family": "minimax/vtp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction / visual tokenization for research and downstream models. Not a text-to-image generator.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6920, "dossier_id": "minimax/vtp/vtp-large-f16d64", "name": "VTP-Large-f16d64", "maker": "minimax", "family": "minimax/vtp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction / visual tokenization for research and downstream models. Not a text-to-image generator.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6921, "dossier_id": "minimax/vtp/vtp-small-f16d64", "name": "VTP-Small-f16d64", "maker": "minimax", "family": "minimax/vtp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction / visual tokenization for research and downstream models. Not a text-to-image generator.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6922, "dossier_id": "missouri/cryo2struct/cryo2struct", "name": "Cryo2Struct", "maker": "missouri", "family": "missouri/cryo2struct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6923, "dossier_id": "missouri/cryo2struct/cryo2struct-2", "name": "Cryo2Struct2", "maker": "missouri", "family": "missouri/cryo2struct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6924, "dossier_id": "missouri/deepdist/deepdist", "name": "DeepDist", "maker": "missouri", "family": "missouri/deepdist", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6925, "dossier_id": "missouri/deepsf/deepsf", "name": "DeepSF", "maker": "missouri", "family": "missouri/deepsf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6926, "dossier_id": "missouri/dncon2/dncon2", "name": "DNCON2", "maker": "missouri", "family": "missouri/dncon2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6927, "dossier_id": "missouri/enqa/enqa", "name": "EnQA", "maker": "missouri", "family": "missouri/enqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6928, "dossier_id": "missouri/flowdock/flowdock", "name": "FlowDock", "maker": "missouri", "family": "missouri/flowdock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6929, "dossier_id": "missouri/gcdm/gcdm", "name": "GCDM", "maker": "missouri", "family": "missouri/gcdm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6930, "dossier_id": "missouri/mulocdeep/mulocdeep", "name": "MULocDeep", "maker": "missouri", "family": "missouri/mulocdeep", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6931, "dossier_id": "missouri/musitedeep/musitedeep", "name": "MusiteDeep", "maker": "missouri", "family": "missouri/musitedeep", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6932, "dossier_id": "mistral/codestral/codestral-24-05", "name": "Codestral", "maker": "mistral", "family": "mistral/codestral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our first cutting-edge language model for coding and FIM, released under the MNPL license.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6933, "dossier_id": "mistral/codestral/codestral-embed-25-05", "name": "Codestral Embed", "maker": "mistral", "family": "mistral/codestral", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our state-of-the-art semantic for extracting representation of code extracts", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our state-of-the-art semantic for extracting representation of code extracts", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6934, "dossier_id": "mistral/codestral/codestral-mamba-7b-0-1", "name": "Codestral Mamba 7B", "maker": "mistral", "family": "mistral/codestral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A coding dedicated model using a mamba 2 architecture for research purposes under an open license.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6935, "dossier_id": "mistral/devstral/devstral-medium-1-0-25-07", "name": "Devstral Medium 1.0", "maker": "mistral", "family": "mistral/devstral", "context_length": null, "prompt_price_per_m": 0.4, "completion_price_per_m": 2.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6936, "dossier_id": "mistral/devstral/devstral-small-1-0-25-05", "name": "Devstral Small 1.0", "maker": "mistral", "family": "mistral/devstral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6937, "dossier_id": "mistral/devstral/devstral-small-1-1-25-07", "name": "Devstral Small 1.1", "maker": "mistral", "family": "mistral/devstral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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)", "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)", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6938, "dossier_id": "mistral/devstral/devstral-small-2-25-12", "name": "Devstral Small 2", "maker": "mistral", "family": "mistral/devstral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our open source model that excels at using tools to explore codebases, editing multiple files and power software engineering agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6939, "dossier_id": "mistral/embed/mistral-embed-23-12", "name": "Mistral Embed", "maker": "mistral", "family": "mistral/embed", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our state-of-the-art semantic for extracting representation of code extracts", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: GA. Our state-of-the-art semantic for extracting representation of code extracts", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6940, "dossier_id": "mistral/leanstral/leanstral-1-5", "name": "Leanstral 1.5", "maker": "mistral", "family": "mistral/leanstral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6941, "dossier_id": "mistral/leanstral/leanstral-26-03", "name": "Leanstral", "maker": "mistral", "family": "mistral/leanstral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6942, "dossier_id": "mistral/lidstral/lidstral-arabic", "name": "LIDstral Arabic", "maker": "mistral", "family": "mistral/lidstral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6943, "dossier_id": "mistral/magistral/magistral-medium-1-0-25-06", "name": "Magistral Medium 1.0", "maker": "mistral", "family": "mistral/magistral", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 5.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our first frontier-class reasoning model released June 2025.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first frontier-class reasoning model released June 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6944, "dossier_id": "mistral/magistral/magistral-medium-1-1-25-07", "name": "Magistral Medium 1.1", "maker": "mistral", "family": "mistral/magistral", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 5.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our frontier-class reasoning model released July 2025.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our frontier-class reasoning model released July 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6945, "dossier_id": "mistral/magistral/magistral-medium-1-2-25-09", "name": "Magistral Medium 1.2", "maker": "mistral", "family": "mistral/magistral", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 5.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our frontier-class multimodal reasoning model update of September 2025.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our frontier-class multimodal reasoning model update of September 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6946, "dossier_id": "mistral/magistral/magistral-small-1-0-25-06", "name": "Magistral Small 1.0", "maker": "mistral", "family": "mistral/magistral", "context_length": null, "prompt_price_per_m": 0.5, "completion_price_per_m": 1.5, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our first small reasoning model released June 2025. Learn more in our [blog post](https://mistral.ai/news/magistral/)", "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/)", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6947, "dossier_id": "mistral/magistral/magistral-small-1-1-25-07", "name": "Magistral Small 1.1", "maker": "mistral", "family": "mistral/magistral", "context_length": null, "prompt_price_per_m": 0.5, "completion_price_per_m": 1.5, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our small reasoning model released July 2025.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our small reasoning model released July 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6948, "dossier_id": "mistral/magistral/magistral-small-1-2-25-09", "name": "Magistral Small 1.2", "maker": "mistral", "family": "mistral/magistral", "context_length": null, "prompt_price_per_m": 0.5, "completion_price_per_m": 1.5, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our small multimodal reasoning model update of September 2025.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our small multimodal reasoning model update of September 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6949, "dossier_id": "mistral/mathstral/mathstral-7b-0-1", "name": "Mathstral 7B", "maker": "mistral", "family": "mistral/mathstral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A math dedicated model for research purposes under an open license.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A math dedicated model for research purposes under an open license.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6950, "dossier_id": "mistral/ministral/ministral-3b-24-1", "name": "Ministral 3B", "maker": "mistral", "family": "mistral/ministral", "context_length": null, "prompt_price_per_m": 0.04, "completion_price_per_m": 0.04, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "World\u2019s best edge model.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. World\u2019s best edge model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6951, "dossier_id": "mistral/mistral/mistral-7b-0-1", "name": "Mistral 7B", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our very first open model, SOTA competing against models up to 2 to 3x larger.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6952, "dossier_id": "mistral/mistral/mistral-7b-0-2", "name": "Mistral 7B", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An update to our first open model with better context.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. An update to our first open model with better context.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6953, "dossier_id": "mistral/mistral/mistral-7b-0-3", "name": "Mistral 7B", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A minor update to our first open model.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A minor update to our first open model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6954, "dossier_id": "mistral/mistral/mistral-large-1-0-24-02", "name": "Mistral Large 1.0", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our first and larger SOTA model for enterprise use cases with powerfull chat capabilities.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6955, "dossier_id": "mistral/mistral/mistral-medium-1-0-23-12", "name": "Mistral Medium 1.0", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our first SOTA enterprise grade model.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. Our first SOTA enterprise grade model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6956, "dossier_id": "mistral/mistral/mistral-medium-3-1-25-08", "name": "Mistral Medium 3.1", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.4, "completion_price_per_m": 2.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our frontier-class multimodal model released August 2025. Improving tone and performance.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our frontier-class multimodal model released August 2025. Improving tone and performance.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6957, "dossier_id": "mistral/mistral/mistral-medium-3-25-05", "name": "Mistral Medium 3", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.4, "completion_price_per_m": 2.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/)", "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/)", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6958, "dossier_id": "mistral/mistral/mistral-next", "name": "Mistral Next", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A prototype model, designed to be brief and concise.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A prototype model, designed to be brief and concise.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6959, "dossier_id": "mistral/mistral/mistral-small-1-0-24-02", "name": "Mistral Small 1.0", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An enterprise grade small model for efficiency.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. An enterprise grade small model for efficiency.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6960, "dossier_id": "mistral/mistral/mistral-small-2-0-24-09", "name": "Mistral Small 2.0", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An update to our efficient small model with open weights under an MRL license.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6961, "dossier_id": "mistral/mistral/mistral-small-3-1-25-03", "name": "Mistral Small 3.1", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A new leader in the small models category with image understanding capabilities, released March 2025.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6962, "dossier_id": "mistral/mistral/mistral-small-creative-25-12", "name": "Mistral Small Creative", "maker": "mistral", "family": "mistral/mistral", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": 0.3, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6963, "dossier_id": "mistral/mixtral/mixtral-8x22b-0-1-0-3", "name": "Mixtral 8x22B", "maker": "mistral", "family": "mistral/mixtral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A large open MoE model competing with much larger dense models.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Retired. A large open MoE model competing with much larger dense models.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6964, "dossier_id": "mistral/mixtral/mixtral-8x7b-0-1", "name": "Mixtral 8x7B", "maker": "mistral", "family": "mistral/mixtral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our first open MoE model, SOTA competing against models up to 2 to 3x larger.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6965, "dossier_id": "mistral/moderation/mistral-moderation-24-11", "name": "Mistral Moderation", "maker": "mistral", "family": "mistral/moderation", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our moderation service that enables our users to detect harmful text content", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our moderation service that enables our users to detect harmful text content", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6966, "dossier_id": "mistral/moderation/mistral-moderation-26-03", "name": "Mistral Moderation 2", "maker": "mistral", "family": "mistral/moderation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6967, "dossier_id": "mistral/ocr/ocr-2-25-05", "name": "OCR 2", "maker": "mistral", "family": "mistral/ocr", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our OCR service powering our Document AI stack that enables our users to extract interleaved text and images", "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", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6968, "dossier_id": "mistral/ocr/ocr-25-03", "name": "OCR", "maker": "mistral", "family": "mistral/ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our first OCR optimized model.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. Our first OCR optimized model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6969, "dossier_id": "mistral/ocr/ocr-3-25-12", "name": "OCR 3", "maker": "mistral", "family": "mistral/ocr", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6970, "dossier_id": "mistral/ocr/ocr-4-0", "name": "OCR 4.0", "maker": "mistral", "family": "mistral/ocr", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our latest OCR service powering our Document AI stack, with native paragraph-level bounding box extraction and structural block labels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6971, "dossier_id": "mistral/ocr/ocr-4-1", "name": "OCR 4.1", "maker": "mistral", "family": "mistral/ocr", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6972, "dossier_id": "mistral/pixtral/pixtral-12b-24-09", "name": "Pixtral 12B", "maker": "mistral", "family": "mistral/pixtral", "context_length": null, "prompt_price_per_m": 0.15, "completion_price_per_m": 0.15, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A 12B model with image understanding capabilities in addition to text.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. A 12B model with image understanding capabilities in addition to text.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6973, "dossier_id": "mistral/pixtral/pixtral-large-24-11", "name": "Pixtral Large", "maker": "mistral", "family": "mistral/pixtral", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 6.0, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/)", "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/)", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6974, "dossier_id": "mistral/shieldstral/shieldstral-1-0", "name": "Shieldstral 1.0", "maker": "mistral", "family": "mistral/shieldstral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6975, "dossier_id": "mistral/timestral/timestral-1-0", "name": "Timestral 1.0", "maker": "mistral", "family": "mistral/timestral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6976, "dossier_id": "mistral/timestral/timestral-1-0-123m", "name": "Timestral 1.0 123M", "maker": "mistral", "family": "mistral/timestral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6977, "dossier_id": "mistral/timestral/timestral-1-0-28m", "name": "Timestral 1.0 28M", "maker": "mistral", "family": "mistral/timestral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6978, "dossier_id": "mistral/timestral/timestral-1-0-5m", "name": "Timestral 1.0 5M", "maker": "mistral", "family": "mistral/timestral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6979, "dossier_id": "mistral/voxtral/voxtral-mini-25-07", "name": "Voxtral Mini", "maker": "mistral", "family": "mistral/voxtral", "context_length": null, "prompt_price_per_m": 0.001, "completion_price_per_m": 0.04, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A mini version of our first audio input model.", "summary_intro": "Mistral AI model, collected 2026-10-15. Card status: Deprecated. A mini version of our first audio input model.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6980, "dossier_id": "mistral/voxtral/voxtral-mini-4b-realtime-arabic", "name": "Voxtral Mini 4B Realtime Arabic", "maker": "mistral", "family": "mistral/voxtral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6981, "dossier_id": "mistral/voxtral/voxtral-mini-transcribe-25-07", "name": "Voxtral Mini Transcribe", "maker": "mistral", "family": "mistral/voxtral", "context_length": null, "prompt_price_per_m": 0.002, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An efficient audio input model, pre-trained and optimized for transcription purposes only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6982, "dossier_id": "mistral/voxtral/voxtral-mini-transcribe-26-02", "name": "Voxtral Mini Transcribe 2", "maker": "mistral", "family": "mistral/voxtral", "context_length": null, "prompt_price_per_m": 0.003, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An efficient audio input model, pre-trained and optimized for transcription purposes only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6983, "dossier_id": "mistral/voxtral/voxtral-mini-transcribe-realtime-26-02", "name": "Voxtral Mini Transcribe Realtime", "maker": "mistral", "family": "mistral/voxtral", "context_length": null, "prompt_price_per_m": 0.006, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An efficient audio input model, pre-trained and optimized for live transcription purposes only.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6984, "dossier_id": "mistral/voxtral/voxtral-tts-26-03", "name": "Voxtral TTS", "maker": "mistral", "family": "mistral/voxtral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 16.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6985, "dossier_id": "mit/ast/ast", "name": "Audio Spectrogram Transformer", "maker": "mit", "family": "mit/ast", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6986, "dossier_id": "mit/boltz/boltz-1", "name": "Boltz-1", "maker": "mit", "family": "mit/boltz", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6987, "dossier_id": "mit/boltz/boltz-2", "name": "Boltz-2", "maker": "mit", "family": "mit/boltz", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6988, "dossier_id": "mit/boltz/boltzgen", "name": "BoltzGen", "maker": "mit", "family": "mit/boltz", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6989, "dossier_id": "mit/dc-ae/dc-ae", "name": "DC-AE", "maker": "mit", "family": "mit/dc-ae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6990, "dossier_id": "mit/diffdock/diffdock", "name": "DiffDock", "maker": "mit", "family": "mit/diffdock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6991, "dossier_id": "mit/diffdock/diffdock-l", "name": "DiffDock-L", "maker": "mit", "family": "mit/diffdock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6992, "dossier_id": "mit/efficientvit/efficientvit", "name": "EfficientViT", "maker": "mit", "family": "mit/efficientvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6993, "dossier_id": "mit/efficientvit/efficientvit-sam", "name": "EfficientViT-SAM", "maker": "mit", "family": "mit/efficientvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6994, "dossier_id": "mit/foreact/foreact", "name": "ForeAct", "maker": "mit", "family": "mit/foreact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6995, "dossier_id": "mit/hart/hart", "name": "HART", "maker": "mit", "family": "mit/hart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6996, "dossier_id": "mit/ladi/ladi-v2-classifier", "name": "LADI v2 classifier", "maker": "mit", "family": "mit/ladi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6997, "dossier_id": "mit/places/places365", "name": "Places365-CNN", "maker": "mit", "family": "mit/places", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6998, "dossier_id": "mit/sana/sana", "name": "SANA", "maker": "mit", "family": "mit/sana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 6999, "dossier_id": "mit/sana/sana-1-5", "name": "SANA-1.5", "maker": "mit", "family": "mit/sana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7000, "dossier_id": "mit/sana/sana-sprint", "name": "SANA-Sprint", "maker": "mit", "family": "mit/sana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7001, "dossier_id": "mit/sana/sana-video", "name": "SANA-Video", "maker": "mit", "family": "mit/sana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7002, "dossier_id": "mit/sana/sana-video-2-0", "name": "SANA-Video 2.0", "maker": "mit", "family": "mit/sana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7003, "dossier_id": "mit/streaming-vlm/streaming-vlm", "name": "StreamingVLM", "maker": "mit", "family": "mit/streaming-vlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7004, "dossier_id": "mit/vila/vila", "name": "VILA", "maker": "mit", "family": "mit/vila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7005, "dossier_id": "mit/vila/vila-1-5", "name": "VILA-1.5", "maker": "mit", "family": "mit/vila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7006, "dossier_id": "mit/vila/vila-u", "name": "VILA-U", "maker": "mit", "family": "mit/vila", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper describes video, image, and language understanding plus visual generation without a separate generation module.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7007, "dossier_id": "modernmt/adaptive/modernmt", "name": "ModernMT", "maker": "modernmt", "family": "modernmt/adaptive", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7008, "dossier_id": "modernmt/qe/mtqe", "name": "ModernMT MTQE", "maker": "modernmt", "family": "modernmt/qe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7009, "dossier_id": "monash/dermfm/dermfm-zero", "name": "DermFM-Zero", "maker": "monash", "family": "monash/dermfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7010, "dossier_id": "monash/dermlip/dermlip", "name": "DermLIP", "maker": "monash", "family": "monash/dermlip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7011, "dossier_id": "monash/make/make", "name": "MAKE", "maker": "monash", "family": "monash/make", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7012, "dossier_id": "monash/panderm/panderm", "name": "PanDerm", "maker": "monash", "family": "monash/panderm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7013, "dossier_id": "monash/upfm/upfm", "name": "Unified Phenotype Foundation Model", "maker": "monash", "family": "monash/upfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7014, "dossier_id": "moonshot/kimi/kimi-k1-5", "name": "Kimi k1.5", "maker": "moonshot", "family": "moonshot/kimi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multimodal long reasoning, as described on the Moonshot GitHub landing page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7015, "dossier_id": "moonshot/kimi/kimi-thinking-preview", "name": "Kimi Thinking Preview", "maker": "moonshot", "family": "moonshot/kimi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Early long-thinking access before Kimi K2 Thinking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7016, "dossier_id": "moonshot/kimi-audio/kimi-audio-7b", "name": "Kimi-Audio-7B", "maker": "moonshot", "family": "moonshot/kimi-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7017, "dossier_id": "moonshot/kimi-audio/kimi-audio-7b-instruct", "name": "Kimi-Audio-7B-Instruct", "maker": "moonshot", "family": "moonshot/kimi-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7018, "dossier_id": "moonshot/kimi-dev/kimi-dev-72b", "name": "Kimi-Dev-72B", "maker": "moonshot", "family": "moonshot/kimi-dev", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7019, "dossier_id": "moonshot/kimina/kimina-prover-preview", "name": "Kimina-Prover Preview", "maker": "moonshot", "family": "moonshot/kimina", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Competition-style formal theorem proving in Lean 4, using a natural-language reasoning trace before the proof.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7020, "dossier_id": "moonshot/moonvit/moonvit-so-400m", "name": "MoonViT-SO-400M", "maker": "moonshot", "family": "moonshot/moonvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Native-resolution visual encoding for Kimi vision-language models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7021, "dossier_id": "mpi/flame/flame", "name": "FLAME", "maker": "mpi", "family": "mpi/flame", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7022, "dossier_id": "mpi/mano/mano", "name": "MANO", "maker": "mpi", "family": "mpi/mano", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7023, "dossier_id": "mpi/skel/bsm", "name": "BSM", "maker": "mpi", "family": "mpi/skel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7024, "dossier_id": "mpi/skel/skel", "name": "SKEL", "maker": "mpi", "family": "mpi/skel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7025, "dossier_id": "mpi/smal/smal", "name": "SMAL", "maker": "mpi", "family": "mpi/smal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7026, "dossier_id": "mpi/smal/smalr", "name": "SMALR", "maker": "mpi", "family": "mpi/smal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7027, "dossier_id": "mpi/smpl/smpl", "name": "SMPL", "maker": "mpi", "family": "mpi/smpl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7028, "dossier_id": "mpi/smpl/smpl-h", "name": "SMPL+H", "maker": "mpi", "family": "mpi/smpl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7029, "dossier_id": "mpi/smpl/smpl-x", "name": "SMPL-X", "maker": "mpi", "family": "mpi/smpl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7030, "dossier_id": "mpi/smpl/star", "name": "STAR", "maker": "mpi", "family": "mpi/smpl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7031, "dossier_id": "mpi/stardist/stardist-2d-paper-dsb2018", "name": "StarDist 2D DSB 2018", "maker": "mpi", "family": "mpi/stardist", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7032, "dossier_id": "mpi/stardist/stardist-2d-versatile-fluo", "name": "StarDist 2D versatile fluo", "maker": "mpi", "family": "mpi/stardist", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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')`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7033, "dossier_id": "mpi/stardist/stardist-2d-versatile-he", "name": "StarDist 2D versatile H&E", "maker": "mpi", "family": "mpi/stardist", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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')`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7034, "dossier_id": "mpi/supr/supr", "name": "SUPR", "maker": "mpi", "family": "mpi/supr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7035, "dossier_id": "mpi/supr/supr-foot", "name": "SUPR-Foot", "maker": "mpi", "family": "mpi/supr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7036, "dossier_id": "mpi/supr/supr-hand", "name": "SUPR-Hand", "maker": "mpi", "family": "mpi/supr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7037, "dossier_id": "mpi/supr/supr-head", "name": "SUPR-Head", "maker": "mpi", "family": "mpi/supr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7038, "dossier_id": "msu/adaface/adaface", "name": "AdaFace", "maker": "msu", "family": "msu/adaface", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7039, "dossier_id": "msu/kp-rpe/kp-rpe", "name": "KP-RPE", "maker": "msu", "family": "msu/kp-rpe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7040, "dossier_id": "msu/topnetmab/topnetmab", "name": "TopNetmAb", "maker": "msu", "family": "msu/topnetmab", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7041, "dossier_id": "msu/topoformer/topoformer", "name": "TopoFormer", "maker": "msu", "family": "msu/topoformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7042, "dossier_id": "muenster/deepbet/deepbet", "name": "deepbet", "maker": "muenster", "family": "muenster/deepbet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7043, "dossier_id": "muenster/deepmriprep/deepmriprep", "name": "deepmriprep", "maker": "muenster", "family": "muenster/deepmriprep", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7044, "dossier_id": "muenster/mccqr/mccqrnn", "name": "MCCQRNN", "maker": "muenster", "family": "muenster/mccqr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7045, "dossier_id": "murf/falcon/falcon", "name": "Falcon", "maker": "murf", "family": "murf/falcon", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7046, "dossier_id": "murf/falcon/falcon-2", "name": "Falcon 2", "maker": "murf", "family": "murf/falcon", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7047, "dossier_id": "murf/gen2/gen2", "name": "Murf Speech Gen 2", "maker": "murf", "family": "murf/gen2", "context_length": null, "prompt_price_per_m": 0.03, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7048, "dossier_id": "nagoya/defsent/defsent-bert-base-uncased", "name": "DefSent BERT base uncased", "maker": "nagoya", "family": "nagoya/defsent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7049, "dossier_id": "nagoya/defsent/defsent-bert-large-uncased", "name": "DefSent BERT large uncased", "maker": "nagoya", "family": "nagoya/defsent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7050, "dossier_id": "nagoya/defsent/defsent-roberta-base", "name": "DefSent RoBERTa base", "maker": "nagoya", "family": "nagoya/defsent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7051, "dossier_id": "nagoya/defsent/defsent-roberta-large", "name": "DefSent RoBERTa large", "maker": "nagoya", "family": "nagoya/defsent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7052, "dossier_id": "nagoya/j-moshi/j-moshi", "name": "J-Moshi", "maker": "nagoya", "family": "nagoya/j-moshi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7053, "dossier_id": "nagoya/j-moshi/j-moshi-ext", "name": "J-Moshi-ext", "maker": "nagoya", "family": "nagoya/j-moshi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7054, "dossier_id": "nagoya/ruri-reranker/ruri-reranker-base", "name": "Ruri reranker base", "maker": "nagoya", "family": "nagoya/ruri-reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7055, "dossier_id": "nagoya/ruri-reranker/ruri-reranker-large", "name": "Ruri reranker large", "maker": "nagoya", "family": "nagoya/ruri-reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7056, "dossier_id": "nagoya/ruri-reranker/ruri-reranker-stage1-base", "name": "Ruri reranker stage 1 base", "maker": "nagoya", "family": "nagoya/ruri-reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Noisy-data stage of the Japanese reranker. The maker's recommended reranker for this size is the stage-2 checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7057, "dossier_id": "nagoya/ruri-reranker/ruri-reranker-stage1-large", "name": "Ruri reranker stage 1 large", "maker": "nagoya", "family": "nagoya/ruri-reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Noisy-data stage of the Japanese reranker. The maker's recommended reranker for this size is the stage-2 checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7058, "dossier_id": "nagoya/ruri-reranker/ruri-reranker-stage1-small", "name": "Ruri reranker stage 1 small", "maker": "nagoya", "family": "nagoya/ruri-reranker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Noisy-data stage of the Japanese reranker. The maker's recommended reranker for this size is the stage-2 checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7059, "dossier_id": "nagoya/shioriha/shioriha-large-reranker", "name": "Shioriha large reranker", "maker": "nagoya", "family": "nagoya/shioriha", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7060, "dossier_id": "nankai/conv2former/conv2former-b", "name": "Conv2Former-B", "maker": "nankai", "family": "nankai/conv2former", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7061, "dossier_id": "nankai/conv2former/conv2former-l", "name": "Conv2Former-L", "maker": "nankai", "family": "nankai/conv2former", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7062, "dossier_id": "nankai/conv2former/conv2former-n", "name": "Conv2Former-N", "maker": "nankai", "family": "nankai/conv2former", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7063, "dossier_id": "nankai/conv2former/conv2former-s", "name": "Conv2Former-S", "maker": "nankai", "family": "nankai/conv2former", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7064, "dossier_id": "nankai/conv2former/conv2former-t", "name": "Conv2Former-T", "maker": "nankai", "family": "nankai/conv2former", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7065, "dossier_id": "nankai/lsknet/lsknet-s", "name": "LSKNet-S", "maker": "nankai", "family": "nankai/lsknet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7066, "dossier_id": "nankai/lsknet/lsknet-t", "name": "LSKNet-T", "maker": "nankai", "family": "nankai/lsknet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7067, "dossier_id": "nankai/stripnet/stripnet-s", "name": "StripNet-S", "maker": "nankai", "family": "nankai/stripnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Remote-sensing oriented detection. The paper reports DOTA, FAIR1M, HRSC2016, and DIOR. The README publishes ImageNet and task checkpoints.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7068, "dossier_id": "nankai/stripnet/stripnet-t", "name": "StripNet-T", "maker": "nankai", "family": "nankai/stripnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7069, "dossier_id": "naples/id-reveal/id-reveal", "name": "ID-Reveal", "maker": "naples", "family": "naples/id-reveal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7070, "dossier_id": "naver/donut/donut", "name": "Donut", "maker": "naver", "family": "naver/donut", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7071, "dossier_id": "naver/hyperclova-x-seed/hyperclova-x-clip", "name": "HyperCLOVA X CLIP", "maker": "naver", "family": "naver/hyperclova-x-seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7072, "dossier_id": "naver/hyperclova-x-seed/seed-4b", "name": "HyperCLOVA X SEED 4B", "maker": "naver", "family": "naver/hyperclova-x-seed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7073, "dossier_id": "ncsu/aognet/aognet-12m", "name": "AOGNet-12M", "maker": "ncsu", "family": "ncsu/aognet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7074, "dossier_id": "ncsu/aognet/aognet-40m", "name": "AOGNet-40M", "maker": "ncsu", "family": "ncsu/aognet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7075, "dossier_id": "nd/arciris/arciris", "name": "ArcIris", "maker": "nd", "family": "nd/arciris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7076, "dossier_id": "nd/eyepreserve/eyepreserve", "name": "EyePreserve", "maker": "nd", "family": "nd/eyepreserve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7077, "dossier_id": "nd/hdbif/hdbif", "name": "HDBIF", "maker": "nd", "family": "nd/hdbif", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7078, "dossier_id": "nd/tripletiris/tripletiris", "name": "TripletIris", "maker": "nd", "family": "nd/tripletiris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7079, "dossier_id": "nec/cotomi/cotomi", "name": "cotomi", "maker": "nec", "family": "nec/cotomi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7080, "dossier_id": "nec/cotomi/cotomi-fast-v2", "name": "cotomi Fast v2", "maker": "nec", "family": "nec/cotomi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7081, "dossier_id": "nec/cotomi/cotomi-light", "name": "cotomi Light", "maker": "nec", "family": "nec/cotomi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7082, "dossier_id": "nec/cotomi/cotomi-pro", "name": "cotomi Pro", "maker": "nec", "family": "nec/cotomi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7083, "dossier_id": "nec/cotomi/cotomi-pro-v2", "name": "cotomi Pro v2", "maker": "nec", "family": "nec/cotomi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7084, "dossier_id": "neu/realign/realign-phi3v", "name": "ReAlign-Phi3v", "maker": "neu", "family": "neu/realign", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7085, "dossier_id": "neu/realign/realign-qwen", "name": "ReAlign-Qwen", "maker": "neu", "family": "neu/realign", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7086, "dossier_id": "neu/rovrm/rovrm", "name": "RoVRM", "maker": "neu", "family": "neu/rovrm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7087, "dossier_id": "neu/techgpt/techgpt", "name": "TechGPT", "maker": "neu", "family": "neu/techgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7088, "dossier_id": "neu/techgpt/techgpt-2-0-qlora", "name": "TechGPT-2.0-QLoRA", "maker": "neu", "family": "neu/techgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7089, "dossier_id": "newcastle/nagl-mbis/nagl-gas", "name": "NAGL-MBIS gas (Q, \u03bc, V)", "maker": "newcastle", "family": "newcastle/nagl-mbis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7090, "dossier_id": "newcastle/nagl-mbis/nagl-v1", "name": "NAGL-MBIS v1", "maker": "newcastle", "family": "newcastle/nagl-mbis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7091, "dossier_id": "newcastle/nagl-mbis/nagl-water", "name": "NAGL-MBIS water (Q, \u03bc, V)", "maker": "newcastle", "family": "newcastle/nagl-mbis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7092, "dossier_id": "niutrans/gram/gram-llama-3-2-3b", "name": "GRAM-LLaMA3.2-3B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7093, "dossier_id": "niutrans/gram/gram-qwen3-1-7b", "name": "GRAM-Qwen3-1.7B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7094, "dossier_id": "niutrans/gram/gram-qwen3-14b", "name": "GRAM-Qwen3-14B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7095, "dossier_id": "niutrans/gram/gram-qwen3-4b", "name": "GRAM-Qwen3-4B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7096, "dossier_id": "niutrans/gram/gram-qwen3-8b", "name": "GRAM-Qwen3-8B-RewardModel", "maker": "niutrans", "family": "niutrans/gram", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7097, "dossier_id": "niutrans/text-api/text-api-flash", "name": "\u6587\u672c\u7ffb\u8bd1API-Flash", "maker": "niutrans", "family": "niutrans/text-api", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7098, "dossier_id": "niutrans/text-api/text-api-pro", "name": "\u6587\u672c\u7ffb\u8bd1API-Pro", "maker": "niutrans", "family": "niutrans/text-api", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7099, "dossier_id": "nju/ddt/ddt-xl-2", "name": "DDT-XL/2", "maker": "nju", "family": "nju/ddt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7100, "dossier_id": "nju/pixnerd/pixnerd-xl-16", "name": "PixNerd-XL/16", "maker": "nju", "family": "nju/pixnerd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7101, "dossier_id": "nju/pixnerd/pixnerd-xxl-16", "name": "PixNerd-XXL/16", "maker": "nju", "family": "nju/pixnerd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7102, "dossier_id": "nju/vfimamba/vfimamba", "name": "VFIMamba", "maker": "nju", "family": "nju/vfimamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7103, "dossier_id": "nju/vfimamba/vfimamba-s", "name": "VFIMamba-S", "maker": "nju", "family": "nju/vfimamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is the efficient VFIMamba version for interpolating frames. The same demos cover 2x and 8x interpolation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7104, "dossier_id": "nju/videomae/videomae-base", "name": "VideoMAE base", "maker": "nju", "family": "nju/videomae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7105, "dossier_id": "nju/videomae/videomae-huge", "name": "VideoMAE huge", "maker": "nju", "family": "nju/videomae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7106, "dossier_id": "nju/videomae/videomae-large", "name": "VideoMAE large", "maker": "nju", "family": "nju/videomae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7107, "dossier_id": "nju/videomae/videomae-small", "name": "VideoMAE small", "maker": "nju", "family": "nju/videomae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7108, "dossier_id": "njust/facialmmt/facialmmt", "name": "FacialMMT", "maker": "njust", "family": "njust/facialmmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7109, "dossier_id": "njust/fs-absa/dutch-restaurant-mt5-small", "name": "Dutch-Restaurant-mT5-Small", "maker": "njust", "family": "njust/fs-absa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7110, "dossier_id": "njust/fs-absa/french-restaurant-mt5-small", "name": "French-Restaurant-mT5-Small", "maker": "njust", "family": "njust/fs-absa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7111, "dossier_id": "nomic/coderank/coderankllm", "name": "CodeRankLLM", "maker": "nomic", "family": "nomic/coderank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Second-stage listwise reranking of code search results.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7112, "dossier_id": "nomic/embed-code/nomic-embed-code", "name": "Nomic Embed Code", "maker": "nomic", "family": "nomic/embed-code", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieving code from natural-language queries in Python, Java, Ruby, PHP, JavaScript, and Go.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7113, "dossier_id": "nomic/embed-multimodal/colnomic-embed-multimodal-3b", "name": "ColNomic Embed Multimodal 3B", "maker": "nomic", "family": "nomic/embed-multimodal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-vector late-interaction retrieval over page images at the 3B size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7114, "dossier_id": "nomic/embed-multimodal/colnomic-embed-multimodal-7b", "name": "ColNomic Embed Multimodal 7B", "maker": "nomic", "family": "nomic/embed-multimodal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Highest-scoring Nomic visual document retriever in the April 2025 release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7115, "dossier_id": "nomic/embed-multimodal/nomic-embed-multimodal-3b", "name": "Nomic Embed Multimodal 3B", "maker": "nomic", "family": "nomic/embed-multimodal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-vector visual document retrieval when you want one vector per page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7116, "dossier_id": "nomic/embed-multimodal/nomic-embed-multimodal-7b", "name": "Nomic Embed Multimodal 7B", "maker": "nomic", "family": "nomic/embed-multimodal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-vector visual document retrieval at the larger Qwen2.5-VL size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7117, "dossier_id": "nomic/embed-vision/nomic-embed-vision-v1", "name": "Nomic Embed Vision v1", "maker": "nomic", "family": "nomic/embed-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7118, "dossier_id": "nomic/embed-vision/nomic-embed-vision-v1-5", "name": "Nomic Embed Vision v1.5", "maker": "nomic", "family": "nomic/embed-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7119, "dossier_id": "nomic/gpt4all/gpt4all-13b-snoozy", "name": "GPT4All-13B-Snoozy", "maker": "nomic", "family": "nomic/gpt4all", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Local assistant-style chat from Nomic's 2023 GPT4All releases.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7120, "dossier_id": "nomic/gpt4all/gpt4all-falcon", "name": "GPT4All-Falcon", "maker": "nomic", "family": "nomic/gpt4all", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Local assistant-style chat from Nomic's 2023 GPT4All releases.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7121, "dossier_id": "nomic/gpt4all/gpt4all-j", "name": "GPT4All-J", "maker": "nomic", "family": "nomic/gpt4all", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Local assistant-style chat from Nomic's 2023 GPT4All releases.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7122, "dossier_id": "nomic/gpt4all/gpt4all-lora", "name": "GPT4All LoRA", "maker": "nomic", "family": "nomic/gpt4all", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The original March 2023 local GPT4All assistant LoRA.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7123, "dossier_id": "nomic/gpt4all/gpt4all-mpt", "name": "GPT4All-MPT", "maker": "nomic", "family": "nomic/gpt4all", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Local assistant-style chat from Nomic's 2023 GPT4All releases.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7124, "dossier_id": "nomic/modernbert-embed/modernbert-embed-base", "name": "ModernBERT Embed Base", "maker": "nomic", "family": "nomic/modernbert-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English embeddings with a ModernBERT encoder and a 256-d Matryoshka option.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7125, "dossier_id": "nomic/parse/nomic-parse", "name": "Nomic Parse", "maker": "nomic", "family": "nomic/parse", "context_length": null, "prompt_price_per_m": 0.012, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Parsing AEC drawings, specs, and PDFs into structured text and figures before Nomic Embed indexes them.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7126, "dossier_id": "northeastern/moxin/moxin-7b-chinese", "name": "Moxin 7B Chinese", "maker": "northeastern", "family": "northeastern/moxin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7127, "dossier_id": "northeastern/moxin/moxin-7b-reasoning", "name": "Moxin 7B Reasoning", "maker": "northeastern", "family": "northeastern/moxin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7128, "dossier_id": "northeastern/moxin/moxin-7b-vla", "name": "Moxin 7B VLA", "maker": "northeastern", "family": "northeastern/moxin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7129, "dossier_id": "northwestern/cell-jepa/cell-jepa", "name": "Cell-JEPA", "maker": "northwestern", "family": "northwestern/cell-jepa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7130, "dossier_id": "northwestern/dnabert/dnabert-2", "name": "DNABERT-2", "maker": "northwestern", "family": "northwestern/dnabert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7131, "dossier_id": "northwestern/dnabert/dnabert-s", "name": "DNABERT-S", "maker": "northwestern", "family": "northwestern/dnabert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7132, "dossier_id": "notredame/eyepreserve/eyepreserve", "name": "EyePreserve", "maker": "notredame", "family": "notredame/eyepreserve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7133, "dossier_id": "notredame/iris/arciris", "name": "ArcIris", "maker": "notredame", "family": "notredame/iris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7134, "dossier_id": "notredame/iris/hdbif", "name": "HDBIF", "maker": "notredame", "family": "notredame/iris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7135, "dossier_id": "notredame/iris/triplet-iris", "name": "TripletIris", "maker": "notredame", "family": "notredame/iris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7136, "dossier_id": "notredame/iris-fm/circlenet", "name": "CircleNet", "maker": "notredame", "family": "notredame/iris-fm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7137, "dossier_id": "notredame/iris-fm/cornernet", "name": "CornerNet (iris canthi)", "maker": "notredame", "family": "notredame/iris-fm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7138, "dossier_id": "notredame/iris-fm/eyelidnet", "name": "EyelidNet", "maker": "notredame", "family": "notredame/iris-fm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7139, "dossier_id": "notredame/iris-fm/h8net", "name": "H8Net", "maker": "notredame", "family": "notredame/iris-fm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "H8Net predicts a projective transform that Notre Dame applies to an off-axis iris image to straighten gaze.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7140, "dossier_id": "notredame/iris-seg/cc-net", "name": "CC-Net (Notre Dame iris)", "maker": "notredame", "family": "notredame/iris-seg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7141, "dossier_id": "notredame/iris-seg/iis", "name": "Infant Iris Segmentation", "maker": "notredame", "family": "notredame/iris-seg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7142, "dossier_id": "notredame/iris-synth/forensic-iris", "name": "Forensic Iris Image Synthesis", "maker": "notredame", "family": "notredame/iris-synth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7143, "dossier_id": "notredame/pbm/pbm", "name": "PBM", "maker": "notredame", "family": "notredame/pbm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7144, "dossier_id": "nottingham/bcll/bcll", "name": "Binarized Convolutional Landmark Localizers", "maker": "nottingham", "family": "nottingham/bcll", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7145, "dossier_id": "nottingham/fan/2d-fan", "name": "2D-FAN", "maker": "nottingham", "family": "nottingham/fan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7146, "dossier_id": "nottingham/fan/2d-to-3d-fan", "name": "2D-to-3D-FAN", "maker": "nottingham", "family": "nottingham/fan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7147, "dossier_id": "nottingham/fan/3d-fan", "name": "3D-FAN", "maker": "nottingham", "family": "nottingham/fan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7148, "dossier_id": "nottingham/fan/3d-fan-depth", "name": "3D-FAN-depth", "maker": "nottingham", "family": "nottingham/fan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7149, "dossier_id": "nottingham/rootnav/rootnav-2", "name": "RootNav 2.0", "maker": "nottingham", "family": "nottingham/rootnav", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7150, "dossier_id": "nottingham/vrn/vrn", "name": "Unguided Volumetric Regression Network", "maker": "nottingham", "family": "nottingham/vrn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7151, "dossier_id": "nous-research/capybara/nous-capybara-3b-v1-9", "name": "Nous Capybara 3B v1.9", "maker": "nous-research", "family": "nous-research/capybara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 3B Capybara chat model, later used as the language backbone of Obsidian 3B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7152, "dossier_id": "nous-research/capybara/nous-capybara-7b-v1", "name": "Nous Capybara 7B v1", "maker": "nous-research", "family": "nous-research/capybara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The first 7B Capybara chat model. The maker later marked v1.9 as the one to use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7153, "dossier_id": "nous-research/capybara/nous-capybara-7b-v1-9", "name": "Nous Capybara 7B v1.9", "maker": "nous-research", "family": "nous-research/capybara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Capybara 7B the card tells users to prefer over v1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7154, "dossier_id": "nous-research/consilience/consilience-40b", "name": "Consilience 40B", "maker": "nous-research", "family": "nous-research/consilience", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7155, "dossier_id": "nous-research/deephermes/deephermes-3-llama-3-3b-preview", "name": "DeepHermes 3 Llama 3 3B Preview", "maker": "nous-research", "family": "nous-research/deephermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7156, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7157, "dossier_id": "nous-research/deephermes/deephermes-3-mistral-24b-preview", "name": "DeepHermes 3 Mistral 24B Preview", "maker": "nous-research", "family": "nous-research/deephermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7158, "dossier_id": "nous-research/deephermes/deephermes-ascensionmaze", "name": "DeepHermes Ascension Maze", "maker": "nous-research", "family": "nous-research/deephermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Maze feedback experiment in the Atropos RL framework. The card says it is an experimental artifact.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7159, "dossier_id": "nous-research/deephermes/deephermes-egregore-v1", "name": "DeepHermes Egregore v1", "maker": "nous-research", "family": "nous-research/deephermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Feedback-testing experiment in the Atropos RL framework. The human preamble is the only part treated as a spec.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7160, "dossier_id": "nous-research/deephermes/deephermes-egregore-v2", "name": "DeepHermes Egregore v2", "maker": "nous-research", "family": "nous-research/deephermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Second feedback-testing Egregore artifact. Same caveat as v1: self-generated claims were not used.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7161, "dossier_id": "nous-research/deephermes/deephermes-financial-fundamentals", "name": "DeepHermes Financial Fundamentals Specialist", "maker": "nous-research", "family": "nous-research/deephermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A narrow experimental model for financial-fundamentals direction prediction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7162, "dossier_id": "nous-research/deephermes/deephermes-toolcalling-specialist", "name": "DeepHermes Tool Calling Specialist", "maker": "nous-research", "family": "nous-research/deephermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A specialist artifact for reasoning-mode tool calling, published with the Atropos RL stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7163, "dossier_id": "nous-research/genstruct/genstruct-7b", "name": "Genstruct 7B", "maker": "nous-research", "family": "nous-research/genstruct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7164, "dossier_id": "nous-research/gpt4-x-vicuna/gpt4-x-vicuna-13b", "name": "GPT4 x Vicuna 13B", "maker": "nous-research", "family": "nous-research/gpt4-x-vicuna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An early Nous instruct tune, before the named Hermes 13B release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7165, "dossier_id": "nous-research/hermes/hermes-2-pro-llama-3-70b", "name": "Hermes 2 Pro Llama 3 70B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 70B function-calling Hermes 2 Pro model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7166, "dossier_id": "nous-research/hermes/hermes-2-pro-llama-3-8b", "name": "Hermes 2 Pro Llama 3 8B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Llama 3 8B function-calling Hermes 2 Pro model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7167, "dossier_id": "nous-research/hermes/hermes-2-pro-mistral-7b", "name": "Hermes 2 Pro Mistral 7B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7168, "dossier_id": "nous-research/hermes/hermes-2-theta-llama-3-70b", "name": "Hermes 2 Theta Llama 3 70B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 70B Hermes 2 Theta merge, aimed at combining Hermes steering with Llama 3 Instruct.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7169, "dossier_id": "nous-research/hermes/hermes-2-theta-llama-3-8b", "name": "Hermes 2 Theta Llama 3 8B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An experimental merged Hermes 2 Pro model meant to combine Hermes steering with Llama 3 Instruct behavior.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7170, "dossier_id": "nous-research/hermes/hermes-3-llama-3-2-3b", "name": "Hermes 3 Llama 3.2 3B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The small Hermes 3 model intended to run on ordinary hardware.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7171, "dossier_id": "nous-research/hermes/hermes-4-14b", "name": "Hermes 4 14B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7172, "dossier_id": "nous-research/hermes/nous-hermes-13b", "name": "Nous Hermes 13B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7173, "dossier_id": "nous-research/hermes/nous-hermes-2-llama-2-70b", "name": "Nous Hermes 2 Llama 2 70B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A ChatML Hermes 2 general assistant on Llama 2 70B, aimed at steerable multi-turn chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7174, "dossier_id": "nous-research/hermes/nous-hermes-2-mistral-7b-dpo", "name": "Nous Hermes 2 Mistral 7B DPO", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7175, "dossier_id": "nous-research/hermes/nous-hermes-2-mixtral-8x7b-dpo", "name": "Nous Hermes 2 Mixtral 8x7B DPO", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The DPO Mixtral Hermes 2 model, the larger Hermes 2 release paired with the SFT checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7176, "dossier_id": "nous-research/hermes/nous-hermes-2-mixtral-8x7b-sft", "name": "Nous Hermes 2 Mixtral 8x7B SFT", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The SFT-only Mixtral Hermes 2 checkpoint, published beside the DPO model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7177, "dossier_id": "nous-research/hermes/nous-hermes-2-solar-10-7b", "name": "Nous Hermes 2 SOLAR 10.7B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A mid-size Hermes 2 instruct model on the SOLAR 10.7B depth-upscaled base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7178, "dossier_id": "nous-research/hermes/nous-hermes-2-vision-alpha", "name": "Nous Hermes 2 Vision Alpha", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7179, "dossier_id": "nous-research/hermes/nous-hermes-2-yi-34b", "name": "Nous Hermes 2 Yi 34B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Hermes 2 instruct model on the Yi 34B base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7180, "dossier_id": "nous-research/hermes/nous-hermes-llama-2-7b", "name": "Nous Hermes Llama 2 7B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A smaller Llama 2 Hermes instruct model, released so the original Hermes behavior could be kept on the newer base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7181, "dossier_id": "nous-research/hermes/nous-hermes-llama2-13b", "name": "Nous Hermes Llama 2 13B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7182, "dossier_id": "nous-research/hermes/nous-hermes-llama2-70b", "name": "Nous Hermes Llama 2 70B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7183, "dossier_id": "nous-research/hermes/redmond-hermes-coder", "name": "Redmond Hermes Coder 15B", "maker": "nous-research", "family": "nous-research/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7184, "dossier_id": "nous-research/nomos/nomos-1", "name": "Nomos 1", "maker": "nous-research", "family": "nous-research/nomos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7185, "dossier_id": "nous-research/nouscoder/nouscoder-14b", "name": "NousCoder 14B", "maker": "nous-research", "family": "nous-research/nouscoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An olympiad-style coding model. Not a general Hermes chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7186, "dossier_id": "nous-research/obsidian/obsidian-3b-v0-5", "name": "Obsidian 3B v0.5", "maker": "nous-research", "family": "nous-research/obsidian", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7187, "dossier_id": "nous-research/olmo-bitnet/olmo-bitnet-1b", "name": "OLMo BitNet 1B", "maker": "nous-research", "family": "nous-research/olmo-bitnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A research checkpoint for 1.58-bit training, not a chat product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7188, "dossier_id": "nous-research/yarn/yarn-llama-2-7b-128k", "name": "Yarn Llama 2 7B 128k", "maker": "nous-research", "family": "nous-research/yarn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A long-context base model extended with YaRN. These are continued pretrains, not Hermes instruct tunes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7189, "dossier_id": "novelai/nai-diffusion/anime-v1-curated", "name": "NovelAI Diffusion Anime V1 Curated", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7190, "dossier_id": "novelai/nai-diffusion/anime-v1-full", "name": "NovelAI Diffusion Anime V1 Full", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7191, "dossier_id": "novelai/nai-diffusion/anime-v2", "name": "NovelAI Diffusion Anime V2", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7192, "dossier_id": "novelai/nai-diffusion/anime-v3", "name": "NovelAI Diffusion Anime V3", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7193, "dossier_id": "novelai/nai-diffusion/furry-beta-v1-3", "name": "NovelAI Diffusion Furry Beta V1.3", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7194, "dossier_id": "novelai/nai-diffusion/furry-v3", "name": "NovelAI Diffusion Furry V3", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7195, "dossier_id": "novelai/nai-diffusion/v4-5-curated", "name": "NovelAI Diffusion V4.5 Curated", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7196, "dossier_id": "novelai/nai-diffusion/v4-5-full", "name": "NovelAI Diffusion V4.5 Full", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7197, "dossier_id": "novelai/nai-diffusion/v4-curated", "name": "NovelAI Diffusion V4 Curated", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7198, "dossier_id": "novelai/nai-diffusion/v4-full", "name": "NovelAI Diffusion V4 Full", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7199, "dossier_id": "novelai/nai-diffusion/v5-curated", "name": "NovelAI Diffusion V5 Curated", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7200, "dossier_id": "novelai/nai-diffusion/v5-full", "name": "NovelAI Diffusion V5 Full", "maker": "novelai", "family": "novelai/nai-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7201, "dossier_id": "novelai/novelai-lm/erato", "name": "Erato", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7202, "dossier_id": "novelai/novelai-lm/genji", "name": "Genji", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7203, "dossier_id": "novelai/novelai-lm/snek", "name": "Snek", "maker": "novelai", "family": "novelai/novelai-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7204, "dossier_id": "ntnu/norgpt/norgpt-23b", "name": "NorGPT-23B", "maker": "ntnu", "family": "ntnu/norgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7205, "dossier_id": "ntnu/norwai-magistral/norwai-magistral-24b", "name": "NorwAI-Magistral-24B", "maker": "ntnu", "family": "ntnu/norwai-magistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7206, "dossier_id": "ntnu/norwai-mistral/norwai-mistral-7b", "name": "NorwAI-Mistral-7B", "maker": "ntnu", "family": "ntnu/norwai-mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7207, "dossier_id": "ntnu/norwai-mistral/norwai-mistral-7b-instruct", "name": "NorwAI-Mistral-7B-instruct", "maker": "ntnu", "family": "ntnu/norwai-mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7208, "dossier_id": "ntnu/norwai-mistral/norwai-mistral-7b-pretrain", "name": "NorwAI-Mistral-7B-pretrain", "maker": "ntnu", "family": "ntnu/norwai-mistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7209, "dossier_id": "ntnu/norwai-mixtral/norwai-mixtral-8x7b", "name": "NorwAI-Mixtral-8x7B", "maker": "ntnu", "family": "ntnu/norwai-mixtral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7210, "dossier_id": "ntnu/norwai-mixtral/norwai-mixtral-8x7b-instruct", "name": "NorwAI-Mixtral-8x7B-instruct", "maker": "ntnu", "family": "ntnu/norwai-mixtral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7211, "dossier_id": "ntt/tsuzumi/tsuzumi-0-6b", "name": "tsuzumi (0.6B)", "maker": "ntt", "family": "ntt/tsuzumi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7212, "dossier_id": "ntt/tsuzumi/tsuzumi-2", "name": "tsuzumi 2", "maker": "ntt", "family": "ntt/tsuzumi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7213, "dossier_id": "ntt/tsuzumi/tsuzumi-7b", "name": "tsuzumi (7B)", "maker": "ntt", "family": "ntt/tsuzumi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7214, "dossier_id": "ntu/basicvsr/basicvsr", "name": "BasicVSR", "maker": "ntu", "family": "ntu/basicvsr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7215, "dossier_id": "ntu/basicvsr/basicvsr-pp", "name": "BasicVSR++", "maker": "ntu", "family": "ntu/basicvsr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7216, "dossier_id": "ntu/basicvsr/iconvsr", "name": "IconVSR", "maker": "ntu", "family": "ntu/basicvsr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7217, "dossier_id": "ntu/basicvsr/realbasicvsr", "name": "RealBasicVSR", "maker": "ntu", "family": "ntu/basicvsr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7218, "dossier_id": "ntu/codeformer/codeformer", "name": "CodeFormer", "maker": "ntu", "family": "ntu/codeformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7219, "dossier_id": "ntu/otter/otter-image-mpt7b", "name": "Otter-Image-MPT7B", "maker": "ntu", "family": "ntu/otter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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}`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7220, "dossier_id": "ntu/otter/otter-video-llama7b", "name": "Otter-Video-LLaMA7B-DenseCaption", "maker": "ntu", "family": "ntu/otter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7221, "dossier_id": "ntu/otter/otterhd", "name": "OtterHD-8B", "maker": "ntu", "family": "ntu/otter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7222, "dossier_id": "ntu/resshift/resshift", "name": "ResShift", "maker": "ntu", "family": "ntu/resshift", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7223, "dossier_id": "ntu/stablesr/stablesr", "name": "StableSR", "maker": "ntu", "family": "ntu/stablesr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7224, "dossier_id": "ntu-taiwan/distilhubert/distilhubert", "name": "DistilHuBERT", "maker": "ntu-taiwan", "family": "ntu-taiwan/distilhubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7225, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7226, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7227, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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+.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7228, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7229, "dossier_id": "ntua/culturemert/culturemert-95m", "name": "CultureMERT-95M", "maker": "ntua", "family": "ntua/culturemert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7230, "dossier_id": "ntua/culturemert/culturemert-ta-95m", "name": "CultureMERT-TA-95M", "maker": "ntua", "family": "ntua/culturemert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7231, "dossier_id": "ntua/ntua-twitter/ntua-twitter-300", "name": "NTUA Twitter word2vec 300d", "maker": "ntua", "family": "ntua/ntua-twitter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7232, "dossier_id": "ntua/ntua-twitter/ntua-twitter-affect-310", "name": "NTUA Twitter affect embeddings 310d", "maker": "ntua", "family": "ntua/ntua-twitter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7233, "dossier_id": "ntua/pyrosage/pyrosage-ames-attentivefp", "name": "Pyrosage AMES AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7234, "dossier_id": "ntua/pyrosage/pyrosage-endocrine-disruption-nr-ahr-attentivefp", "name": "Pyrosage Endocrine Disruption NR-AhR AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7235, "dossier_id": "ntua/pyrosage/pyrosage-endocrine-disruption-nr-ar-attentivefp", "name": "Pyrosage Endocrine Disruption NR-AR AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7236, "dossier_id": "ntua/pyrosage/pyrosage-endocrine-disruption-nr-aromatase-attentivefp", "name": "Pyrosage Endocrine Disruption NR-aromatase AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7237, "dossier_id": "ntua/pyrosage/pyrosage-endocrine-disruption-nr-er-attentivefp", "name": "Pyrosage Endocrine Disruption NR-ER AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7238, "dossier_id": "ntua/pyrosage/pyrosage-fba-attentivefp", "name": "Pyrosage FBA AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7239, "dossier_id": "ntua/pyrosage/pyrosage-fbc-attentivefp", "name": "Pyrosage FBC AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7240, "dossier_id": "ntua/pyrosage/pyrosage-irritation-corrosion-eye-corrosion-attentivefp", "name": "Pyrosage Eye Corrosion AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7241, "dossier_id": "ntua/pyrosage/pyrosage-irritation-corrosion-eye-irritation-attentivefp", "name": "Pyrosage Eye Irritation AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7242, "dossier_id": "ntua/pyrosage/pyrosage-kaoh-attentivefp", "name": "Pyrosage kAOH AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7243, "dossier_id": "ntua/pyrosage/pyrosage-kh-attentivefp", "name": "Pyrosage KH AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7244, "dossier_id": "ntua/pyrosage/pyrosage-koa-attentivefp", "name": "Pyrosage KOA AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7245, "dossier_id": "ntua/pyrosage/pyrosage-koc-attentivefp", "name": "Pyrosage KOC AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7246, "dossier_id": "ntua/pyrosage/pyrosage-kow-attentivefp", "name": "Pyrosage KOW AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7247, "dossier_id": "ntua/pyrosage/pyrosage-lc50-attentivefp", "name": "Pyrosage LC50 AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7248, "dossier_id": "ntua/pyrosage/pyrosage-ld50-zhu-attentivefp", "name": "Pyrosage LD50 Zhu AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7249, "dossier_id": "ntua/pyrosage/pyrosage-pka-acidic-attentivefp", "name": "Pyrosage pKa acidic AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7250, "dossier_id": "ntua/pyrosage/pyrosage-pka-basic-attentivefp", "name": "Pyrosage pKa basic AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7251, "dossier_id": "ntua/pyrosage/pyrosage-plv-attentivefp", "name": "Pyrosage PLV AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7252, "dossier_id": "ntua/pyrosage/pyrosage-sw-attentivefp", "name": "Pyrosage SW AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7253, "dossier_id": "ntua/pyrosage/pyrosage-tbiodeg-attentivefp", "name": "Pyrosage tbiodeg AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7254, "dossier_id": "ntua/pyrosage/pyrosage-tbp-attentivefp", "name": "Pyrosage TBP AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7255, "dossier_id": "ntua/pyrosage/pyrosage-tfishbio-attentivefp", "name": "Pyrosage tfishbio AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7256, "dossier_id": "ntua/pyrosage/pyrosage-tmp-attentivefp", "name": "Pyrosage TMP AttentiveFP", "maker": "ntua", "family": "ntua/pyrosage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7257, "dossier_id": "nuaa/geomoe/geomoe", "name": "GeoMoE", "maker": "nuaa", "family": "nuaa/geomoe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7258, "dossier_id": "nuaa/geomoe/geomoe-l", "name": "GeoMoE-L", "maker": "nuaa", "family": "nuaa/geomoe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7259, "dossier_id": "nudt/saratr/sar-jepa", "name": "SAR-JEPA", "maker": "nudt", "family": "nudt/saratr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7260, "dossier_id": "nudt/saratr/saratr-x", "name": "SARATR-X", "maker": "nudt", "family": "nudt/saratr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7261, "dossier_id": "nudt/saratr/saratr-x-v2", "name": "SARATR-X-v2", "maker": "nudt", "family": "nudt/saratr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7262, "dossier_id": "nus/d-ar/d-ar-l", "name": "D-AR-L", "maker": "nus", "family": "nus/d-ar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7263, "dossier_id": "nus/d-ar/d-ar-xl", "name": "D-AR-XL", "maker": "nus", "family": "nus/d-ar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7264, "dossier_id": "nus/egovlp/egovlp", "name": "EgoVLP", "maker": "nus", "family": "nus/egovlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The project says the pretrained model transfers to egocentric retrieval, action recognition, and Ego4D localization tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7265, "dossier_id": "nus/greco/greco", "name": "GRECO", "maker": "nus", "family": "nus/greco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says GRECO estimates whether a corrected sentence is grammatical so that GEC system outputs can be reranked.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7266, "dossier_id": "nus/magicanimate/magicanimate", "name": "MagicAnimate", "maker": "nus", "family": "nus/magicanimate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The GitHub page says it animates a reference image with a motion sequence while keeping appearance over time.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7267, "dossier_id": "nus/makeanything/makeanything", "name": "MakeAnything", "maker": "nus", "family": "nus/makeanything", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7268, "dossier_id": "nus/mitty/mitty", "name": "Mitty", "maker": "nus", "family": "nus/mitty", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The paper says Mitty turns a human demonstration video into a robot-execution video without action labels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7269, "dossier_id": "nus/next-gpt/next-gpt", "name": "NExT-GPT", "maker": "nus", "family": "nus/next-gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7270, "dossier_id": "nus/omniconsistency/omniconsistency", "name": "OmniConsistency", "maker": "nus", "family": "nus/omniconsistency", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7271, "dossier_id": "nus/show-1/show-1", "name": "Show-1", "maker": "nus", "family": "nus/show-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7272, "dossier_id": "nus/show-harness/show-harness", "name": "Show-Harness", "maker": "nus", "family": "nus/show-harness", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7273, "dossier_id": "nus/show-o/show-o", "name": "Show-o", "maker": "nus", "family": "nus/show-o", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7274, "dossier_id": "nus/show-o/show-o2-1-5b", "name": "Show-o2 1.5B", "maker": "nus", "family": "nus/show-o", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7275, "dossier_id": "nus/show-o/show-o2-7b", "name": "Show-o2 7B", "maker": "nus", "family": "nus/show-o", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7276, "dossier_id": "nus/showui/showui-2b", "name": "ShowUI 2B", "maker": "nus", "family": "nus/showui", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7277, "dossier_id": "nus/showui/showui-pi", "name": "ShowUI-\u03c0", "maker": "nus", "family": "nus/showui", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7278, "dossier_id": "nvidia/alpamayo/alpamayo-1", "name": "Alpamayo 1", "maker": "nvidia", "family": "nvidia/alpamayo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7279, "dossier_id": "nvidia/alpamayo/alpamayo-1-5", "name": "Alpamayo 1.5", "maker": "nvidia", "family": "nvidia/alpamayo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Interactive reasoning engine for rare driving scenes: video, history, navigation, and questions in; trajectories and reasoning traces out.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7280, "dossier_id": "nvidia/alpamayo/alpamayo-2-super", "name": "Alpamayo 2 Super", "maker": "nvidia", "family": "nvidia/alpamayo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7281, "dossier_id": "nvidia/canary/canary-1b-v2", "name": "Canary 1B v2", "maker": "nvidia", "family": "nvidia/canary", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual speech recognition. The card cites NVIDIA Granary and NeMo ASR set 3.0 as training data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7282, "dossier_id": "nvidia/canary/canary-qwen-2-5b", "name": "Canary-Qwen 2.5B", "maker": "nvidia", "family": "nvidia/canary", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Automatic speech recognition in the Canary line, at a larger size than Canary 1B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7283, "dossier_id": "nvidia/cosmos/cosmos-embed1", "name": "Cosmos Embed 1", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed video for retrieval and downstream physical-AI classifiers.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7284, "dossier_id": "nvidia/cosmos/cosmos-nemotron-34b", "name": "Cosmos Nemotron 34B", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7285, "dossier_id": "nvidia/cosmos/cosmos-predict1", "name": "Cosmos Predict 1", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-world and video-to-world generation for robot and autonomous-vehicle development, including synthetic future video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7286, "dossier_id": "nvidia/cosmos/cosmos-predict2", "name": "Cosmos Predict 2", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7287, "dossier_id": "nvidia/cosmos/cosmos-predict2-5", "name": "Cosmos Predict 2.5", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Simulate future world states for robots and vehicles, including post-training on domain video and multiview camera layouts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7288, "dossier_id": "nvidia/cosmos/cosmos-reason1", "name": "Cosmos Reason 1", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reason over images and video for robotics and other physical-AI tasks, and answer in text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7289, "dossier_id": "nvidia/cosmos/cosmos-transfer1", "name": "Cosmos Transfer 1", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Augment simulation and 3D inputs such as segmentation, depth, LiDAR, and boxes into realistic video for physical-AI training.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7290, "dossier_id": "nvidia/cosmos/cosmos-transfer2-5", "name": "Cosmos Transfer 2.5", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Turn simulation and 3D spatial controls into more varied photorealistic video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7291, "dossier_id": "nvidia/cosmos/cosmos3-edge", "name": "Cosmos 3 Edge", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time robot policy and visual reasoning on Jetson and workstation GPUs.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7292, "dossier_id": "nvidia/cosmos/cosmos3-nano", "name": "Cosmos 3 Nano", "maker": "nvidia", "family": "nvidia/cosmos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster physical reasoning and world generation when Super is too large.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7293, "dossier_id": "nvidia/edify/edify-360-hdri", "name": "Edify 360 HDRi", "maker": "nvidia", "family": "nvidia/edify", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Light 3D scenes and provide backgrounds. Shutterstock offered an early-access 360 HDRi generator based on Edify.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7294, "dossier_id": "nvidia/edify/edify-3d", "name": "Edify 3D", "maker": "nvidia", "family": "nvidia/edify", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7295, "dossier_id": "nvidia/edify/edify-image", "name": "Edify Image", "maker": "nvidia", "family": "nvidia/edify", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Enterprise image generation from licensed training data. NVIDIA described higher speed and prompt controls in the 2024 Getty update.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7296, "dossier_id": "nvidia/embed/nemotron-3-embed-1b", "name": "Nemotron 3 Embed 1B", "maker": "nvidia", "family": "nvidia/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings for retrieval, at a smaller size than Nemotron 3 Embed 8B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7297, "dossier_id": "nvidia/embed/nemotron-3-embed-8b", "name": "Nemotron 3 Embed 8B", "maker": "nvidia", "family": "nvidia/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings for semantic search and retrieval in the Nemotron retrieval stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7298, "dossier_id": "nvidia/embed/nv-embed-v1", "name": "NV-Embed v1", "maker": "nvidia", "family": "nvidia/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It produces text embeddings for retrieval and semantic search.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7299, "dossier_id": "nvidia/embed/nv-embed-v2", "name": "NV-Embed v2", "maker": "nvidia", "family": "nvidia/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings for retrieval, classification, and semantic search. The non-commercial license limits production use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7300, "dossier_id": "nvidia/gr00t/gr00t-h", "name": "GR00T-H", "maker": "nvidia", "family": "nvidia/gr00t", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7301, "dossier_id": "nvidia/gr00t/gr00t-n1", "name": "Isaac GR00T N1", "maker": "nvidia", "family": "nvidia/gr00t", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7302, "dossier_id": "nvidia/gr00t/gr00t-n1-5", "name": "Isaac GR00T N1.5", "maker": "nvidia", "family": "nvidia/gr00t", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base vision-language-action checkpoint for fine-tuning humanoid manipulation skills.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7303, "dossier_id": "nvidia/gr00t/gr00t-n1-6", "name": "Isaac GR00T N1.6", "maker": "nvidia", "family": "nvidia/gr00t", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generalist humanoid manipulation from language and images, including sim-to-real post-training.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7304, "dossier_id": "nvidia/gr00t/gr00t-n1-7", "name": "Isaac GR00T N1.7", "maker": "nvidia", "family": "nvidia/gr00t", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generalist robot policy base: language and camera images in, continuous actions out, for fine-tuning on downstream tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7305, "dossier_id": "nvidia/kumo/kumo-tabular", "name": "Kumo Tabular", "maker": "nvidia", "family": "nvidia/kumo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predict class probabilities or regression targets for new rows by conditioning on labeled example rows from the user's table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7306, "dossier_id": "nvidia/magpie/magpie-tts-multilingual", "name": "Magpie TTS Multilingual", "maker": "nvidia", "family": "nvidia/magpie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7307, "dossier_id": "nvidia/nemoguard/nemotron-content-safety-reasoning-4b", "name": "Nemotron Content Safety Reasoning 4B", "maker": "nvidia", "family": "nvidia/nemoguard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The name and the Nemotron safety collection indicate content-safety classification with reasoning. The exact label schema was not read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7308, "dossier_id": "nvidia/nemotron/llama-3-1-nemotron-70b-instruct", "name": "Llama 3.1 Nemotron 70B Instruct", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7309, "dossier_id": "nvidia/nemotron/llama-3-1-nemotron-nano-4b", "name": "Llama 3.1 Nemotron Nano 4B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It is the compact Nano size for local or low-cost reasoning experiments in the Llama Nemotron generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7310, "dossier_id": "nvidia/nemotron/llama-3-1-nemotron-nano-8b", "name": "Llama 3.1 Nemotron Nano 8B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "NVIDIA groups Nano models as the efficient end of the Nemotron reasoning line for targeted tasks.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7311, "dossier_id": "nvidia/nemotron/llama-3-1-nemotron-ultra-253b", "name": "Llama 3.1 Nemotron Ultra 253B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7312, "dossier_id": "nvidia/nemotron/llama-3-3-nemotron-super-49b", "name": "Llama 3.3 Nemotron Super 49B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7313, "dossier_id": "nvidia/nemotron/nemotron-3-5-asr-streaming", "name": "Nemotron 3.5 ASR Streaming 0.6B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-latency multilingual automatic speech recognition for voice agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7314, "dossier_id": "nvidia/nemotron/nemotron-3-5-bolt", "name": "Nemotron 3.5 Bolt 74B-A9B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7315, "dossier_id": "nvidia/nemotron/nemotron-3-5-bolt-e4b", "name": "Nemotron 3.5 Bolt E4B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7316, "dossier_id": "nvidia/nemotron/nemotron-4-340b", "name": "Nemotron-4 340B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7317, "dossier_id": "nvidia/nemotron/nemotron-cascade-8b", "name": "Nemotron-Cascade 8B", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "NVIDIA presents it as a general-purpose reasoning model that can run with or without an explicit thinking pass.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7318, "dossier_id": "nvidia/nemotron/nemotron-nano-12b-v2-vl", "name": "Nemotron Nano 12B v2 VL", "maker": "nvidia", "family": "nvidia/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7319, "dossier_id": "nvidia/parakeet/parakeet-tdt-0-6b-v3", "name": "Parakeet TDT 0.6B v3", "maker": "nvidia", "family": "nvidia/parakeet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7320, "dossier_id": "nwpu/c2ser/c2ser-llm", "name": "C2SER-LLM", "maker": "nwpu", "family": "nwpu/c2ser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7321, "dossier_id": "nwpu/c2ser/emotion2vec-s", "name": "Emotion2Vec-S", "maker": "nwpu", "family": "nwpu/c2ser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7322, "dossier_id": "nwpu/diffrhythm/diffrhythm", "name": "DiffRhythm", "maker": "nwpu", "family": "nwpu/diffrhythm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7323, "dossier_id": "nwpu/diffrhythm/diffrhythm-2", "name": "DiffRhythm 2", "maker": "nwpu", "family": "nwpu/diffrhythm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7324, "dossier_id": "nwpu/easy-turn/easy-turn", "name": "Easy Turn", "maker": "nwpu", "family": "nwpu/easy-turn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7325, "dossier_id": "nwpu/llase/llase-g1", "name": "LLaSE-G1", "maker": "nwpu", "family": "nwpu/llase", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7326, "dossier_id": "nwpu/meanvc/meanvc", "name": "MeanVC", "maker": "nwpu", "family": "nwpu/meanvc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7327, "dossier_id": "nwpu/meanvc/meanvc-2", "name": "MeanVC2", "maker": "nwpu", "family": "nwpu/meanvc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7328, "dossier_id": "nwpu/msvae/msvae", "name": "MSVAE", "maker": "nwpu", "family": "nwpu/msvae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7329, "dossier_id": "nwpu/omnicodec/omnicodec", "name": "OmniCodec", "maker": "nwpu", "family": "nwpu/omnicodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7330, "dossier_id": "nwpu/osum/osum", "name": "OSUM", "maker": "nwpu", "family": "nwpu/osum", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.\"", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7331, "dossier_id": "nwpu/osum/osum-echat", "name": "OSUM-EChat", "maker": "nwpu", "family": "nwpu/osum", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7332, "dossier_id": "nwpu/osum/osum-pangu", "name": "OSUM-Pangu", "maker": "nwpu", "family": "nwpu/osum", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7333, "dossier_id": "nwpu/sembridge/sembridge", "name": "SemBridge", "maker": "nwpu", "family": "nwpu/sembridge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7334, "dossier_id": "nwpu/sense/sense", "name": "SenSE", "maker": "nwpu", "family": "nwpu/sense", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7335, "dossier_id": "nwpu/skyeye/skyeyegpt", "name": "SkyEyeGPT", "maker": "nwpu", "family": "nwpu/skyeye", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7336, "dossier_id": "nwpu/songformer/songformer", "name": "SongFormer", "maker": "nwpu", "family": "nwpu/songformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7337, "dossier_id": "nwpu/speaker-reasoner/speaker-reasoner", "name": "Speaker-Reasoner", "maker": "nwpu", "family": "nwpu/speaker-reasoner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7338, "dossier_id": "nwpu/spherevae/spherevae", "name": "SphereVAE", "maker": "nwpu", "family": "nwpu/spherevae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7339, "dossier_id": "nwpu/yingmusic/yingmusic-singer-plus", "name": "YingMusic-Singer-Plus", "maker": "nwpu", "family": "nwpu/yingmusic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7340, "dossier_id": "nyu/cambrian/cambrian-13b", "name": "Cambrian-1 13B", "maker": "nyu", "family": "nyu/cambrian", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7341, "dossier_id": "nyu/cambrian/cambrian-34b", "name": "Cambrian-1 34B", "maker": "nyu", "family": "nyu/cambrian", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7342, "dossier_id": "nyu/cambrian/cambrian-8b", "name": "Cambrian-1 8B", "maker": "nyu", "family": "nyu/cambrian", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7343, "dossier_id": "nyu/cambrian/cambrian-phi3-3b", "name": "Cambrian-1 Phi-3 3B", "maker": "nyu", "family": "nyu/cambrian", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7344, "dossier_id": "nyu/cambrian-p/cambrian-p-7b", "name": "Cambrian-P 7B", "maker": "nyu", "family": "nyu/cambrian-p", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7345, "dossier_id": "nyu/cambrian-s/cambrian-s-0-5b", "name": "Cambrian-S 0.5B", "maker": "nyu", "family": "nyu/cambrian-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7346, "dossier_id": "nyu/cambrian-s/cambrian-s-1-5b", "name": "Cambrian-S 1.5B", "maker": "nyu", "family": "nyu/cambrian-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7347, "dossier_id": "nyu/cambrian-s/cambrian-s-3b", "name": "Cambrian-S 3B", "maker": "nyu", "family": "nyu/cambrian-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7348, "dossier_id": "nyu/cambrian-s/cambrian-s-7b-lfp", "name": "Cambrian-S 7B LFP", "maker": "nyu", "family": "nyu/cambrian-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7349, "dossier_id": "nyu/freeflow/freeflow-xl", "name": "FreeFlow-XL", "maker": "nyu", "family": "nyu/freeflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7350, "dossier_id": "nyu/miniberta/miniberta-base-10m", "name": "MiniBERTa base 10M", "maker": "nyu", "family": "nyu/miniberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7351, "dossier_id": "nyu/miniberta/miniberta-base-1b", "name": "MiniBERTa base 1B", "maker": "nyu", "family": "nyu/miniberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7352, "dossier_id": "nyu/miniberta/miniberta-med-small-1m", "name": "MiniBERTa med-small 1M", "maker": "nyu", "family": "nyu/miniberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7353, "dossier_id": "nyu/nyutron/nyutron", "name": "NYUTron", "maker": "nyu", "family": "nyu/nyutron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7354, "dossier_id": "nyu/rae/rae-ditdh-xl", "name": "RAE DiTDH-XL", "maker": "nyu", "family": "nyu/rae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7355, "dossier_id": "nyu/raev2/raev2", "name": "RAEv2", "maker": "nyu", "family": "nyu/raev2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7356, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7357, "dossier_id": "nyu/scale-rae/scale-rae-qwen1-5b-dit2-4b-webssl", "name": "Scale-RAE WebSSL", "maker": "nyu", "family": "nyu/scale-rae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7358, "dossier_id": "nyu/scale-rae/scale-rae-qwen7b-dit9-8b", "name": "Scale-RAE Qwen 7B DiT 9.8B", "maker": "nyu", "family": "nyu/scale-rae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7359, "dossier_id": "nyu/sit/sit-xl", "name": "SiT-XL", "maker": "nyu", "family": "nyu/sit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7360, "dossier_id": "nyu/solaris/solaris", "name": "Solaris", "maker": "nyu", "family": "nyu/solaris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7361, "dossier_id": "openai/audio/gpt-transcribe", "name": "GPT-Transcribe", "maker": "openai", "family": "openai/audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-accuracy speech-to-text model for file and Realtime input transcription", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7362, "dossier_id": "openai/clip/clip", "name": "CLIP", "maker": "openai", "family": "openai/clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contrastive model that learns visual concepts from natural language.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7363, "dossier_id": "openai/dalle/dall-e-2", "name": "DALL\u00b7E 2", "maker": "openai", "family": "openai/dalle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deprecated image generation model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7364, "dossier_id": "openai/dalle/dall-e-3", "name": "DALL\u00b7E 3", "maker": "openai", "family": "openai/dalle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Deprecated image generation model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7365, "dossier_id": "openai/embedding/text-embedding-3-large", "name": "text-embedding-3-large", "maker": "openai", "family": "openai/embedding", "context_length": null, "prompt_price_per_m": 0.13, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Most capable embedding model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7366, "dossier_id": "openai/embedding/text-embedding-3-small", "name": "text-embedding-3-small", "maker": "openai", "family": "openai/embedding", "context_length": null, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small embedding model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7367, "dossier_id": "openai/embedding/text-embedding-ada-002", "name": "text-embedding-ada-002", "maker": "openai", "family": "openai/embedding", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Older embedding model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7368, "dossier_id": "openai/gpt/ada", "name": "ada", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 1.6, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id ada. No model-page dossier was available on 2026-10-15.", "summary_intro": "OpenAI's ada (`ada`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id ada.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7369, "dossier_id": "openai/gpt/babbage", "name": "babbage", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 2.4, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id babbage. No model-page dossier was available on 2026-10-15.", "summary_intro": "OpenAI's babbage (`babbage`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id babbage.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7370, "dossier_id": "openai/gpt/babbage-002", "name": "babbage-002", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 0.4, "completion_price_per_m": 0.4, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replacement for the GPT-3 ada and babbage base models", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7371, "dossier_id": "openai/gpt/code-cushman-001", "name": "code-cushman-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-cushman-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7372, "dossier_id": "openai/gpt/code-cushman-002", "name": "code-cushman-002", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-cushman-002. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7373, "dossier_id": "openai/gpt/code-davinci-001", "name": "code-davinci-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-davinci-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7374, "dossier_id": "openai/gpt/code-davinci-002", "name": "code-davinci-002", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-davinci-002. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7375, "dossier_id": "openai/gpt/code-davinci-edit-001", "name": "code-davinci-edit-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-davinci-edit-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7376, "dossier_id": "openai/gpt/code-search-ada-code-001", "name": "code-search-ada-code-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-search-ada-code-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7377, "dossier_id": "openai/gpt/code-search-ada-text-001", "name": "code-search-ada-text-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-search-ada-text-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7378, "dossier_id": "openai/gpt/code-search-babbage-code-001", "name": "code-search-babbage-code-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-search-babbage-code-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7379, "dossier_id": "openai/gpt/code-search-babbage-text-001", "name": "code-search-babbage-text-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id code-search-babbage-text-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7380, "dossier_id": "openai/gpt/curie", "name": "curie", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 12.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id curie. No model-page dossier was available on 2026-10-15.", "summary_intro": "OpenAI's curie (`curie`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id curie.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7381, "dossier_id": "openai/gpt/davinci", "name": "davinci", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id davinci. No model-page dossier was available on 2026-10-15.", "summary_intro": "OpenAI's davinci (`davinci`) is a gpt model. Status on 2026-10-15: retired. Confidence: high. OpenAI API model id davinci.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7382, "dossier_id": "openai/gpt/davinci-002", "name": "davinci-002", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 2.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replacement for the GPT-3 curie and davinci base models", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7383, "dossier_id": "openai/gpt/ft-babbage-002", "name": "ft-babbage-002", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id ft-babbage-002. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7384, "dossier_id": "openai/gpt/ft-davinci-002", "name": "ft-davinci-002", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id ft-davinci-002. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7385, "dossier_id": "openai/gpt/ft-gpt-3-5-turbo", "name": "ft-gpt-3.5-turbo", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id ft-gpt-3.5-turbo. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7386, "dossier_id": "openai/gpt/ft-gpt-4", "name": "ft-gpt-4", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id ft-gpt-4. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7387, "dossier_id": "openai/gpt/gpt", "name": "GPT", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "2018 transformer language model. GPT-2's announcement calls GPT-2 its successor.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7388, "dossier_id": "openai/gpt/gpt-2", "name": "GPT-2", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1.5B transformer language model. Full weights released November 5, 2019.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7389, "dossier_id": "openai/gpt/gpt-3", "name": "GPT-3", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "175B autoregressive language model announced May 28, 2020.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7390, "dossier_id": "openai/gpt/gpt-4-1106-vision-preview", "name": "gpt-4-1106-vision-preview", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": 30.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id gpt-4-1106-vision-preview. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7391, "dossier_id": "openai/gpt/gpt-4-32k", "name": "gpt-4-32k", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 60.0, "completion_price_per_m": 120.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id gpt-4-32k. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7392, "dossier_id": "openai/gpt/gpt-4-vision-preview", "name": "gpt-4-vision-preview", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": 30.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id gpt-4-vision-preview. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7393, "dossier_id": "openai/gpt/gpt-4o-audio", "name": "gpt-4o-audio", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id gpt-4o-audio. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7394, "dossier_id": "openai/gpt/gpt-4o-mini-audio", "name": "gpt-4o-mini-audio", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id gpt-4o-mini-audio. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7395, "dossier_id": "openai/gpt/gpt-5-4-cyber", "name": "gpt-5.4-cyber", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id gpt-5.4-cyber. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7396, "dossier_id": "openai/gpt/gpt-5-5-cyber", "name": "GPT-5.5 Cyber", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 12.5, "completion_price_per_m": 75.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Daybreak cyber model id on the pricing page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7397, "dossier_id": "openai/gpt/gpt-5-search-api", "name": "GPT-5 Search API", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 1.25, "completion_price_per_m": 10.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Search model id published on the pricing page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7398, "dossier_id": "openai/gpt/instructgpt", "name": "InstructGPT", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following models trained from GPT-3 with human feedback.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7399, "dossier_id": "openai/gpt/text-ada-001", "name": "text-ada-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 0.4, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-ada-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7400, "dossier_id": "openai/gpt/text-babbage-001", "name": "text-babbage-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 0.5, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-babbage-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7401, "dossier_id": "openai/gpt/text-curie-001", "name": "text-curie-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-curie-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7402, "dossier_id": "openai/gpt/text-davinci-001", "name": "text-davinci-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 20.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-davinci-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7403, "dossier_id": "openai/gpt/text-davinci-002", "name": "text-davinci-002", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 20.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-davinci-002. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7404, "dossier_id": "openai/gpt/text-davinci-003", "name": "text-davinci-003", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 20.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-davinci-003. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7405, "dossier_id": "openai/gpt/text-davinci-edit-001", "name": "text-davinci-edit-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-davinci-edit-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7406, "dossier_id": "openai/gpt/text-search-ada-doc-001", "name": "text-search-ada-doc-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-search-ada-doc-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7407, "dossier_id": "openai/gpt/text-search-ada-query-001", "name": "text-search-ada-query-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-search-ada-query-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7408, "dossier_id": "openai/gpt/text-search-babbage-doc-001", "name": "text-search-babbage-doc-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-search-babbage-doc-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7409, "dossier_id": "openai/gpt/text-search-babbage-query-001", "name": "text-search-babbage-query-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-search-babbage-query-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7410, "dossier_id": "openai/gpt/text-search-curie-doc-001", "name": "text-search-curie-doc-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 20.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-search-curie-doc-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7411, "dossier_id": "openai/gpt/text-search-curie-query-001", "name": "text-search-curie-query-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 20.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-search-curie-query-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7412, "dossier_id": "openai/gpt/text-search-davinci-doc-001", "name": "text-search-davinci-doc-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 200.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-search-davinci-doc-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7413, "dossier_id": "openai/gpt/text-search-davinci-query-001", "name": "text-search-davinci-query-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 200.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-search-davinci-query-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7414, "dossier_id": "openai/gpt/text-similarity-ada-001", "name": "text-similarity-ada-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-similarity-ada-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7415, "dossier_id": "openai/gpt/text-similarity-babbage-001", "name": "text-similarity-babbage-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-similarity-babbage-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7416, "dossier_id": "openai/gpt/text-similarity-curie-001", "name": "text-similarity-curie-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 20.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-similarity-curie-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7417, "dossier_id": "openai/gpt/text-similarity-davinci-001", "name": "text-similarity-davinci-001", "maker": "openai", "family": "openai/gpt", "context_length": null, "prompt_price_per_m": 200.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-similarity-davinci-001. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7418, "dossier_id": "openai/gpt-image/chatgpt-image-latest", "name": "chatgpt-image-latest", "maker": "openai", "family": "openai/gpt-image", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 32.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Previous image model used in ChatGPT.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7419, "dossier_id": "openai/gpt-image/gpt-image-1", "name": "GPT-Image-1", "maker": "openai", "family": "openai/gpt-image", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 40.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our previous image generation model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7420, "dossier_id": "openai/gpt-image/gpt-image-1-5", "name": "GPT-Image-1.5", "maker": "openai", "family": "openai/gpt-image", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 32.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our previous image generation model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7421, "dossier_id": "openai/gpt-image/gpt-image-1-mini", "name": "GPT-Image-1 Mini", "maker": "openai", "family": "openai/gpt-image", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 8.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A cost-efficient version of GPT Image 1", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7422, "dossier_id": "openai/gpt-image/gpt-image-2", "name": "GPT-Image-2", "maker": "openai", "family": "openai/gpt-image", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "State-of-the-art image generation model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7423, "dossier_id": "openai/gpt-image/gpt-image-2-5-flare", "name": "GPT-Image-2.5 Flare", "maker": "openai", "family": "openai/gpt-image", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast, high-quality everyday image generation", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7424, "dossier_id": "openai/gpt-image/gpt-image-2-5-sunburst", "name": "GPT-Image-2.5 Sunburst", "maker": "openai", "family": "openai/gpt-image", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our most capable model for image generation and editing", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7425, "dossier_id": "openai/moderation/omni-moderation-latest", "name": "omni-moderation", "maker": "openai", "family": "openai/moderation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Identify potentially harmful content in text and images", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7426, "dossier_id": "openai/moderation/text-moderation-007", "name": "text-moderation-007", "maker": "openai", "family": "openai/moderation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id text-moderation-007. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7427, "dossier_id": "openai/moderation/text-moderation-latest", "name": "text-moderation", "maker": "openai", "family": "openai/moderation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Previous generation text-only moderation model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7428, "dossier_id": "openai/moderation/text-moderation-stable", "name": "text-moderation-stable", "maker": "openai", "family": "openai/moderation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Previous generation text-only moderation model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7429, "dossier_id": "openai/realtime/gpt-4o-mini-realtime", "name": "gpt-4o-mini-realtime", "maker": "openai", "family": "openai/realtime", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id gpt-4o-mini-realtime. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7430, "dossier_id": "openai/realtime/gpt-4o-realtime", "name": "gpt-4o-realtime", "maker": "openai", "family": "openai/realtime", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenAI API model id gpt-4o-realtime. No model-page dossier was available on 2026-10-15.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7431, "dossier_id": "openai/realtime/gpt-live-1", "name": "GPT-Live 1", "maker": "openai", "family": "openai/realtime", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Our premier model for natural, expressive voice conversations with smooth interruption handling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7432, "dossier_id": "openai/realtime/gpt-live-transcribe", "name": "GPT-Live-Transcribe", "maker": "openai", "family": "openai/realtime", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-latency speech-to-text model for realtime transcription", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7433, "dossier_id": "openai/rosalind/gpt-rosalind-discovery", "name": "GPT-Rosalind Discovery", "maker": "openai", "family": "openai/rosalind", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 25.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Second Rosalind model id on the pricing page, same published token rates.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7434, "dossier_id": "openai/rosalind/gpt-rosalind-research", "name": "GPT-Rosalind", "maker": "openai", "family": "openai/rosalind", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 25.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Life-sciences reasoning for approved organizations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7435, "dossier_id": "openai/sora/sora", "name": "Sora", "maker": "openai", "family": "openai/sora", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-video model announced February 15, 2024.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7436, "dossier_id": "openai/sora/sora-2", "name": "Sora 2", "maker": "openai", "family": "openai/sora", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flagship video generation with synced audio", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7437, "dossier_id": "openai/sora/sora-2-pro", "name": "Sora 2 Pro", "maker": "openai", "family": "openai/sora", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Most advanced synced-audio video generation", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7438, "dossier_id": "openai/tts/gpt-4o-mini-tts", "name": "GPT-4o Mini TTS", "maker": "openai", "family": "openai/tts", "context_length": null, "prompt_price_per_m": 0.6, "completion_price_per_m": 12.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech model powered by GPT-4o Mini", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7439, "dossier_id": "openai/tts/tts-1", "name": "TTS-1", "maker": "openai", "family": "openai/tts", "context_length": null, "prompt_price_per_m": 15.0, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech model optimized for speed", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7440, "dossier_id": "openai/tts/tts-1-hd", "name": "TTS-1 HD", "maker": "openai", "family": "openai/tts", "context_length": null, "prompt_price_per_m": 30.0, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-speech model optimized for quality", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7441, "dossier_id": "openai/whisper/whisper", "name": "Whisper", "maker": "openai", "family": "openai/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-source speech recognition model announced September 21, 2022.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7442, "dossier_id": "openai/whisper/whisper-1", "name": "Whisper", "maker": "openai", "family": "openai/whisper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General-purpose speech recognition model", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7443, "dossier_id": "oppo/a2fm/a2fm-32b-rl", "name": "A2FM-32B-rl", "maker": "oppo", "family": "oppo/a2fm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7444, "dossier_id": "oppo/afm/afm-codeagent-32b-rl", "name": "AFM-CodeAgent-32B-rl", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7445, "dossier_id": "oppo/afm/afm-codeagent-32b-sft", "name": "AFM-CodeAgent-32B-sft", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7446, "dossier_id": "oppo/afm/afm-codeagent-7b-rl", "name": "AFM-CodeAgent-7B-rl", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7447, "dossier_id": "oppo/afm/afm-codeagent-7b-sft", "name": "AFM-CodeAgent-7B-sft", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7448, "dossier_id": "oppo/afm/afm-mhqa-agent-3b-rl", "name": "AFM-MHQA-Agent-3B-rl", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7449, "dossier_id": "oppo/afm/afm-mhqa-agent-3b-sft", "name": "AFM-MHQA-Agent-3B-sft", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7450, "dossier_id": "oppo/afm/afm-mhqa-agent-7b-rl", "name": "AFM-MHQA-Agent-7B-rl", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7451, "dossier_id": "oppo/afm/afm-mhqa-agent-7b-sft", "name": "AFM-MHQA-Agent-7B-sft", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7452, "dossier_id": "oppo/afm/afm-webagent-32b-rl", "name": "AFM-WebAgent-32B-rl", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7453, "dossier_id": "oppo/afm/afm-webagent-32b-sft", "name": "AFM-WebAgent-32B-sft", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7454, "dossier_id": "oppo/afm/afm-webagent-7b-rl", "name": "AFM-WebAgent-7B-rl", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7455, "dossier_id": "oppo/afm/afm-webagent-7b-sft", "name": "AFM-WebAgent-7B-sft", "maker": "oppo", "family": "oppo/afm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7456, "dossier_id": "oppo/andesgpt/andesgpt", "name": "AndesGPT", "maker": "oppo", "family": "oppo/andesgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7457, "dossier_id": "oppo/andesgpt/andesgpt-2-0", "name": "AndesGPT-2.0", "maker": "oppo", "family": "oppo/andesgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7458, "dossier_id": "oppo/andesgpt/andesgpt-tiny", "name": "AndesGPT-Tiny", "maker": "oppo", "family": "oppo/andesgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7459, "dossier_id": "oppo/andesgpt/andesgpt-titan", "name": "AndesGPT-Titan", "maker": "oppo", "family": "oppo/andesgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7460, "dossier_id": "oppo/andesgpt/andesgpt-turbo", "name": "AndesGPT-Turbo", "maker": "oppo", "family": "oppo/andesgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7461, "dossier_id": "oppo/andesvl/andesvl-0-6b-instruct", "name": "AndesVL-0.6B-Instruct", "maker": "oppo", "family": "oppo/andesvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7462, "dossier_id": "oppo/andesvl/andesvl-0-6b-thinking", "name": "AndesVL-0.6B-Thinking", "maker": "oppo", "family": "oppo/andesvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7463, "dossier_id": "oppo/andesvl/andesvl-1b-instruct", "name": "AndesVL-1B-Instruct", "maker": "oppo", "family": "oppo/andesvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7464, "dossier_id": "oppo/andesvl/andesvl-1b-thinking", "name": "AndesVL-1B-Thinking", "maker": "oppo", "family": "oppo/andesvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7465, "dossier_id": "oppo/andesvl/andesvl-2b-instruct", "name": "AndesVL-2B-Instruct", "maker": "oppo", "family": "oppo/andesvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7466, "dossier_id": "oppo/andesvl/andesvl-2b-thinking", "name": "AndesVL-2B-Thinking", "maker": "oppo", "family": "oppo/andesvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7467, "dossier_id": "oppo/andesvl/andesvl-4b-instruct", "name": "AndesVL-4B-Instruct", "maker": "oppo", "family": "oppo/andesvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7468, "dossier_id": "oppo/andesvl/andesvl-4b-thinking", "name": "AndesVL-4B-Thinking", "maker": "oppo", "family": "oppo/andesvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7469, "dossier_id": "oppo/chaos/chaos", "name": "CHAOS", "maker": "oppo", "family": "oppo/chaos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7470, "dossier_id": "oppo/cutetts/cutetts", "name": "CuteTTS", "maker": "oppo", "family": "oppo/cutetts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7471, "dossier_id": "oppo/cutetts/cutetts-distill", "name": "CuteTTS-distill", "maker": "oppo", "family": "oppo/cutetts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7472, "dossier_id": "oppo/o-researcher/o-researcher-72b-rl", "name": "O-Researcher-72B-rl", "maker": "oppo", "family": "oppo/o-researcher", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7473, "dossier_id": "oppo/o-researcher/o-researcher-72b-sft", "name": "O-Researcher-72B-sft", "maker": "oppo", "family": "oppo/o-researcher", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7474, "dossier_id": "oppo/obert/obert-100m", "name": "OBERT-100M", "maker": "oppo", "family": "oppo/obert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7475, "dossier_id": "oppo/obert/obert-1b", "name": "OBERT-1B", "maker": "oppo", "family": "oppo/obert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7476, "dossier_id": "oppo/obert/obert-300m", "name": "OBERT-300M", "maker": "oppo", "family": "oppo/obert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7477, "dossier_id": "oppo/smtl/smtl-30b", "name": "SMTL-30B", "maker": "oppo", "family": "oppo/smtl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7478, "dossier_id": "opus-clip/clip/clip-anything", "name": "ClipAnything", "maker": "opus-clip", "family": "opus-clip/clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7479, "dossier_id": "opus-clip/clip/clip-basic", "name": "ClipBasic", "maker": "opus-clip", "family": "opus-clip/clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7480, "dossier_id": "opus-clip/reframe/reframe-anything", "name": "ReframeAnything", "maker": "opus-clip", "family": "opus-clip/reframe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7481, "dossier_id": "oregon/cct/cct-14", "name": "CCT-14", "maker": "oregon", "family": "oregon/cct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7482, "dossier_id": "oregon/cct/cct-7", "name": "CCT-7", "maker": "oregon", "family": "oregon/cct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7483, "dossier_id": "oregon/dinat/dinat-base", "name": "DiNAT-Base", "maker": "oregon", "family": "oregon/dinat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7484, "dossier_id": "oregon/dinat/dinat-large", "name": "DiNAT-Large", "maker": "oregon", "family": "oregon/dinat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7485, "dossier_id": "oregon/dinat/dinat-mini", "name": "DiNAT-Mini", "maker": "oregon", "family": "oregon/dinat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7486, "dossier_id": "oregon/dinat/dinat-small", "name": "DiNAT-Small", "maker": "oregon", "family": "oregon/dinat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7487, "dossier_id": "oregon/dinat/dinat-tiny", "name": "DiNAT-Tiny", "maker": "oregon", "family": "oregon/dinat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7488, "dossier_id": "oregon/dinats/dinats-base", "name": "DiNAT-s Base", "maker": "oregon", "family": "oregon/dinats", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7489, "dossier_id": "oregon/dinats/dinats-large", "name": "DiNAT-s Large", "maker": "oregon", "family": "oregon/dinats", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7490, "dossier_id": "oregon/dinats/dinats-small", "name": "DiNAT-s Small", "maker": "oregon", "family": "oregon/dinats", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7491, "dossier_id": "oregon/dinats/dinats-tiny", "name": "DiNAT-s Tiny", "maker": "oregon", "family": "oregon/dinats", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7492, "dossier_id": "oregon/matting-anything/matting-anything", "name": "Matting Anything", "maker": "oregon", "family": "oregon/matting-anything", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7493, "dossier_id": "oregon/nat/nat-base", "name": "NAT-Base", "maker": "oregon", "family": "oregon/nat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7494, "dossier_id": "oregon/nat/nat-mini", "name": "NAT-Mini", "maker": "oregon", "family": "oregon/nat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7495, "dossier_id": "oregon/nat/nat-small", "name": "NAT-Small", "maker": "oregon", "family": "oregon/nat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7496, "dossier_id": "oregon/nat/nat-tiny", "name": "NAT-Tiny", "maker": "oregon", "family": "oregon/nat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7497, "dossier_id": "oregon/oneformer/oneformer-ade20k", "name": "OneFormer ADE20K", "maker": "oregon", "family": "oregon/oneformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Universal semantic, instance, and panoptic segmentation on ADE20K. The README row stored as primary is Swin-L at 640.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7498, "dossier_id": "oregon/oneformer/oneformer-cityscapes", "name": "OneFormer Cityscapes", "maker": "oregon", "family": "oregon/oneformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Universal segmentation on Cityscapes val. The README row stored as primary is Swin-L.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7499, "dossier_id": "oregon/oneformer/oneformer-coco", "name": "OneFormer COCO", "maker": "oregon", "family": "oregon/oneformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Universal segmentation on COCO val. The README row stored as primary is Swin-L.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7500, "dossier_id": "oregon/oneformer/oneformer-mapillary", "name": "OneFormer Mapillary Vistas", "maker": "oregon", "family": "oregon/oneformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7501, "dossier_id": "oregon/stylenat/stylenat", "name": "StyleNAT", "maker": "oregon", "family": "oregon/stylenat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7502, "dossier_id": "oregon/versatile-diffusion/versatile-diffusion", "name": "Versatile Diffusion", "maker": "oregon", "family": "oregon/versatile-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7503, "dossier_id": "oregonstate/pointconv/pointconv", "name": "PointConv", "maker": "oregonstate", "family": "oregonstate/pointconv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7504, "dossier_id": "oregonstate/pointpwc/pointpwc-net", "name": "PointPWC-Net", "maker": "oregonstate", "family": "oregonstate/pointpwc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7505, "dossier_id": "osaka/mace-osaka24/mace-osaka24", "name": "MACE-Osaka24", "maker": "osaka", "family": "osaka/mace-osaka24", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7506, "dossier_id": "oslo/gpt-bert/gpt-bert-babylm-base", "name": "GPT-BERT BabyLM 100M", "maker": "oslo", "family": "oslo/gpt-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7507, "dossier_id": "oslo/gpt-bert/gpt-bert-babylm-small", "name": "GPT-BERT BabyLM 10M", "maker": "oslo", "family": "oslo/gpt-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English GPT-BERT modeling on the 10-million-word BabyLM track.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7508, "dossier_id": "oslo/ltg-bert/ltg-bert-babylm", "name": "LTG-BERT BabyLM", "maker": "oslo", "family": "oslo/ltg-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English sample-efficient masked language modeling on the BabyLM shared-task corpus.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7509, "dossier_id": "oslo/ltg-bert/ltg-bert-bnc", "name": "LTG-BERT BNC", "maker": "oslo", "family": "oslo/ltg-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7510, "dossier_id": "oslo/norbert/norbert3-base", "name": "NorBERT 3 base", "maker": "oslo", "family": "oslo/norbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7511, "dossier_id": "oslo/norbert/norbert3-large", "name": "NorBERT 3 large", "maker": "oslo", "family": "oslo/norbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7512, "dossier_id": "oslo/norbert/norbert3-small", "name": "NorBERT 3 small", "maker": "oslo", "family": "oslo/norbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7513, "dossier_id": "oslo/norbert/norbert3-xs", "name": "NorBERT 3 xs", "maker": "oslo", "family": "oslo/norbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7514, "dossier_id": "oslo/norelmo/norelmo-100", "name": "NorELMo100", "maker": "oslo", "family": "oslo/norelmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7515, "dossier_id": "oslo/norelmo/norelmo-30", "name": "NorELMo30", "maker": "oslo", "family": "oslo/norelmo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7516, "dossier_id": "oslo/normistral/normistral-11b-thinking", "name": "NorMistral-11B-thinking", "maker": "oslo", "family": "oslo/normistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7517, "dossier_id": "oslo/normistral/normistral-11b-translate", "name": "NorMistral-11B-translate", "maker": "oslo", "family": "oslo/normistral", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7518, "dossier_id": "oslo/nort5/nort5-base", "name": "NorT5 base", "maker": "oslo", "family": "oslo/nort5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Norwegian text-to-text generation, including the card's masked-span example and a prefix-LM style continuation after finetuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7519, "dossier_id": "oslo/nort5/nort5-large", "name": "NorT5 large", "maker": "oslo", "family": "oslo/nort5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Norwegian text-to-text generation, including the card's masked-span example and a prefix-LM style continuation after finetuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7520, "dossier_id": "oslo/nort5/nort5-small", "name": "NorT5 small", "maker": "oslo", "family": "oslo/nort5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Norwegian text-to-text generation, including the card's masked-span example and a prefix-LM style continuation after finetuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7521, "dossier_id": "oslo/nort5/nort5-xs", "name": "NorT5 xs", "maker": "oslo", "family": "oslo/nort5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Norwegian text-to-text generation, including the card's masked-span example and a prefix-LM style continuation after finetuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7522, "dossier_id": "oslo/slide/slide-base", "name": "SLIDE-base", "maker": "oslo", "family": "oslo/slide", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7523, "dossier_id": "oslo/slide/slide-fast", "name": "SLIDE-fast", "maker": "oslo", "family": "oslo/slide", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7524, "dossier_id": "oslo/slide/slide-small", "name": "SLIDE-small", "maker": "oslo", "family": "oslo/slide", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7525, "dossier_id": "oslo/slide/slide-x-small", "name": "SLIDE-x-small", "maker": "oslo", "family": "oslo/slide", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7526, "dossier_id": "osu/amplegcg/amplegcg", "name": "AmpleGCG", "maker": "osu", "family": "osu/amplegcg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7527, "dossier_id": "osu/amplegcg/amplegcg-plus", "name": "AmpleGCG-Plus", "maker": "osu", "family": "osu/amplegcg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7528, "dossier_id": "osu/attrscore/attrscore-flan-t5", "name": "AttrScore Flan-T5", "maker": "osu", "family": "osu/attrscore", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7529, "dossier_id": "osu/autosdt/autosdt-coder-14b", "name": "AutoSDT-Coder-14B", "maker": "osu", "family": "osu/autosdt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same AutoSDT-5K coding fine-tune as the 7B model, at the Qwen2.5-Coder-14B-Instruct size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7530, "dossier_id": "osu/autosdt/autosdt-coder-7b", "name": "AutoSDT-Coder-7B", "maker": "osu", "family": "osu/autosdt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7531, "dossier_id": "osu/beetleflow/beetleflow", "name": "BeetleFlow", "maker": "osu", "family": "osu/beetleflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7532, "dossier_id": "osu/bioclip/biocap", "name": "BioCAP", "maker": "osu", "family": "osu/bioclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7533, "dossier_id": "osu/bioclip/bioclip", "name": "BioCLIP", "maker": "osu", "family": "osu/bioclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7534, "dossier_id": "osu/bioclip/bioclip-2", "name": "BioCLIP 2", "maker": "osu", "family": "osu/bioclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7535, "dossier_id": "osu/bioclip/bioclip-2-5", "name": "BioCLIP 2.5", "maker": "osu", "family": "osu/bioclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Species recognition and broader biological visual tasks at a larger ViT-H/14 scale than BioCLIP 2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7536, "dossier_id": "osu/biovocabbert/biovocabbert", "name": "BioVocabBERT", "maker": "osu", "family": "osu/biovocabbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7537, "dossier_id": "osu/dreamer/dreamer-72b", "name": "Dreamer-72B", "maker": "osu", "family": "osu/dreamer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same web-simulation planning role as Dreamer-7B, at the 72B Qwen2-VL scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7538, "dossier_id": "osu/dreamer/dreamer-7b", "name": "Dreamer-7B", "maker": "osu", "family": "osu/dreamer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7539, "dossier_id": "osu/early-experience/early-experience-14b", "name": "Early Experience 14B", "maker": "osu", "family": "osu/early-experience", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7540, "dossier_id": "osu/early-experience/early-experience-7b", "name": "Early Experience 7B", "maker": "osu", "family": "osu/early-experience", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7541, "dossier_id": "osu/gui-drag/gui-drag-3b", "name": "GUI-Drag-3B", "maker": "osu", "family": "osu/gui-drag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7542, "dossier_id": "osu/gui-drag/gui-drag-7b", "name": "GUI-Drag-7B", "maker": "osu", "family": "osu/gui-drag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7543, "dossier_id": "osu/intr/intr", "name": "INTR", "maker": "osu", "family": "osu/intr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7544, "dossier_id": "osu/llasmol/llasmol-codellama-7b", "name": "LlaSMol-CodeLlama-7B", "maker": "osu", "family": "osu/llasmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7545, "dossier_id": "osu/llasmol/llasmol-galactica-6-7b", "name": "LlaSMol-Galactica-6.7B", "maker": "osu", "family": "osu/llasmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7546, "dossier_id": "osu/llasmol/llasmol-llama2-7b", "name": "LlaSMol-Llama2-7B", "maker": "osu", "family": "osu/llasmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7547, "dossier_id": "osu/llasmol/llasmol-mistral-7b", "name": "LlaSMol-Mistral-7B", "maker": "osu", "family": "osu/llasmol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7548, "dossier_id": "osu/magicbrush/hive-magicbrush", "name": "HIVE-MagicBrush", "maker": "osu", "family": "osu/magicbrush", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7549, "dossier_id": "osu/magicbrush/instructpix2pix-magicbrush", "name": "InstructPix2Pix-MagicBrush", "maker": "osu", "family": "osu/magicbrush", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7550, "dossier_id": "osu/mindact/mindact-action-prediction", "name": "MindAct action prediction", "maker": "osu", "family": "osu/mindact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7551, "dossier_id": "osu/mindact/mindact-candidate-generation", "name": "MindAct candidate generation", "maker": "osu", "family": "osu/mindact", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7552, "dossier_id": "osu/mmla/mmla", "name": "MMLA", "maker": "osu", "family": "osu/mmla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7553, "dossier_id": "osu/prompt-cam/prompt-cam", "name": "Prompt-CAM", "maker": "osu", "family": "osu/prompt-cam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7554, "dossier_id": "osu/quest/quest-2b", "name": "QUEST-2B", "maker": "osu", "family": "osu/quest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7555, "dossier_id": "osu/quest/quest-30b", "name": "QUEST-30B", "maker": "osu", "family": "osu/quest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7556, "dossier_id": "osu/quest/quest-35b", "name": "QUEST-35B", "maker": "osu", "family": "osu/quest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7557, "dossier_id": "osu/quest/quest-4b", "name": "QUEST-4B", "maker": "osu", "family": "osu/quest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7558, "dossier_id": "osu/quest/quest-9b", "name": "QUEST-9B", "maker": "osu", "family": "osu/quest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7559, "dossier_id": "osu/reasonbert/reasonbert-bert-base", "name": "ReasonBERT-BERT-base", "maker": "osu", "family": "osu/reasonbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7560, "dossier_id": "osu/reasonbert/reasonbert-roberta-base", "name": "ReasonBERT-RoBERTa-base", "maker": "osu", "family": "osu/reasonbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7561, "dossier_id": "osu/reasonbert/reasonbert-tapas-base", "name": "ReasonBERT-TAPAS-base", "maker": "osu", "family": "osu/reasonbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7562, "dossier_id": "osu/trait2vec/trait2vec", "name": "Trait2Vec", "maker": "osu", "family": "osu/trait2vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7563, "dossier_id": "osu/uground/uground", "name": "UGround", "maker": "osu", "family": "osu/uground", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7564, "dossier_id": "osu/uground/uground-v1-2b", "name": "UGround-V1-2B", "maker": "osu", "family": "osu/uground", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7565, "dossier_id": "osu/uground/uground-v1-72b", "name": "UGround-V1-72B", "maker": "osu", "family": "osu/uground", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same GUI point-grounding job as the smaller V1 models, at the 72B Qwen2-VL scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7566, "dossier_id": "osu/uground/uground-v1-7b", "name": "UGround-V1-7B", "maker": "osu", "family": "osu/uground", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7567, "dossier_id": "osu/webjudge/webjudge-7b", "name": "WebJudge-7B", "maker": "osu", "family": "osu/webjudge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7568, "dossier_id": "oxford/ablang/ablang", "name": "AbLang", "maker": "oxford", "family": "oxford/ablang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7569, "dossier_id": "oxford/ablang/ablang2", "name": "AbLang-2", "maker": "oxford", "family": "oxford/ablang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7570, "dossier_id": "oxford/ablang/tcrlang-paired", "name": "TCRLang-Paired", "maker": "oxford", "family": "oxford/ablang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embeddings and masked likelihoods for paired TCR sequences, using the same interface as AbLang-2 except alignment.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7571, "dossier_id": "oxford/anarcii/anarcii", "name": "ANARCII", "maker": "oxford", "family": "oxford/anarcii", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7572, "dossier_id": "oxford/antifold/antifold", "name": "AntiFold", "maker": "oxford", "family": "oxford/antifold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7573, "dossier_id": "oxford/countgd/countgd", "name": "CountGD", "maker": "oxford", "family": "oxford/countgd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7574, "dossier_id": "oxford/countgd/countgd-plus-plus", "name": "CountGD++", "maker": "oxford", "family": "oxford/countgd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7575, "dossier_id": "oxford/immunebuilder/abodybuilder2", "name": "ABodyBuilder2", "maker": "oxford", "family": "oxford/immunebuilder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7576, "dossier_id": "oxford/immunebuilder/nanobodybuilder2", "name": "NanoBodyBuilder2", "maker": "oxford", "family": "oxford/immunebuilder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predict a nanobody structure from one heavy-chain sequence, with the same ensemble uncertainty idea as the other ImmuneBuilder models.", "summary_intro": "NanoBodyBuilder2 is the nanobody structure model released with ImmuneBuilder by the Oxford Protein Informatics Group. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7577, "dossier_id": "oxford/immunebuilder/tcrbuilder2", "name": "TCRBuilder2", "maker": "oxford", "family": "oxford/immunebuilder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predict TCR alpha-beta structures from sequence, with an ensemble error estimate, as the TCR member of the ImmuneBuilder set.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7578, "dossier_id": "oxford/vgg/vgg-16", "name": "VGG-16", "maker": "oxford", "family": "oxford/vgg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7579, "dossier_id": "oxford/vgg/vgg-19", "name": "VGG-19", "maker": "oxford", "family": "oxford/vgg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7580, "dossier_id": "oxford/vgg/vgg-f", "name": "VGG-F", "maker": "oxford", "family": "oxford/vgg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7581, "dossier_id": "oxford/vgg/vgg-m", "name": "VGG-M", "maker": "oxford", "family": "oxford/vgg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7582, "dossier_id": "oxford/vgg/vgg-s", "name": "VGG-S", "maker": "oxford", "family": "oxford/vgg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7583, "dossier_id": "oxford/vgg-face/vgg-face", "name": "VGG-Face", "maker": "oxford", "family": "oxford/vgg-face", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7584, "dossier_id": "oxford/vggface2/resnet-50", "name": "VGGFace2 ResNet-50", "maker": "oxford", "family": "oxford/vggface2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7585, "dossier_id": "oxford/vggface2/se-resnet-50", "name": "VGGFace2 SE-ResNet-50", "maker": "oxford", "family": "oxford/vggface2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Face verification embeddings, including compact 128-d and 256-d variants, evaluated by the authors on IJB-B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7586, "dossier_id": "padova/wav2taste/wav2taste", "name": "wav2taste", "maker": "padova", "family": "padova/wav2taste", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Padova model wav2taste, from the Centro di Sonologia Computazionale (Department of Information Engineering). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7587, "dossier_id": "panasonic/hipie/hipie", "name": "HIPIE", "maker": "panasonic", "family": "panasonic/hipie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7588, "dossier_id": "panasonic/lavida/lavida-d", "name": "LaViDa-D", "maker": "panasonic", "family": "panasonic/lavida", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7589, "dossier_id": "panasonic/lavida/lavida-l", "name": "LaViDa-L", "maker": "panasonic", "family": "panasonic/lavida", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7590, "dossier_id": "panasonic/omniflow/omniflow", "name": "OmniFlow", "maker": "panasonic", "family": "panasonic/omniflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7591, "dossier_id": "panasonic/panasonic-llm/panasonic-llm-100b", "name": "Panasonic-LLM-100b", "maker": "panasonic", "family": "panasonic/panasonic-llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7592, "dossier_id": "panasonic/segllm/segllm", "name": "SegLLM", "maker": "panasonic", "family": "panasonic/segllm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7593, "dossier_id": "panasonic/uniegomotion/uniegomotion", "name": "UniEgoMotion", "maker": "panasonic", "family": "panasonic/uniegomotion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7594, "dossier_id": "papercup/pl-bert/multilingual-pl-bert", "name": "Multilingual PL-BERT", "maker": "papercup", "family": "papercup/pl-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7595, "dossier_id": "papercup/xlpt/xlpt", "name": "XLPT", "maker": "papercup", "family": "papercup/xlpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7596, "dossier_id": "paris-saclay/novae/novae-brain-0", "name": "Novae brain-0", "maker": "paris-saclay", "family": "paris-saclay/novae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7597, "dossier_id": "paris-saclay/novae/novae-human-0", "name": "Novae human-0", "maker": "paris-saclay", "family": "paris-saclay/novae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7598, "dossier_id": "paris-saclay/novae/novae-mouse-0", "name": "Novae mouse-0", "maker": "paris-saclay", "family": "paris-saclay/novae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7599, "dossier_id": "paris-saclay/novae/novae-scconcept-multi-species", "name": "Novae scConcept multi-species", "maker": "paris-saclay", "family": "paris-saclay/novae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7600, "dossier_id": "peking/align-anything/aa-chameleon-7b-base", "name": "Align-Anything Chameleon 7B Base", "maker": "peking", "family": "peking/align-anything", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7601, "dossier_id": "peking/align-anything/aa-chameleon-7b-plus", "name": "Align-Anything Chameleon 7B Plus", "maker": "peking", "family": "peking/align-anything", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7602, "dossier_id": "peking/align-ds/align-ds-v", "name": "Align-DS-V", "maker": "peking", "family": "peking/align-ds", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Align-DS-V is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7603, "dossier_id": "peking/aligner/aligner-7b-v1-0", "name": "Aligner-7B v1.0", "maker": "peking", "family": "peking/aligner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Aligner-7B v1.0 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7604, "dossier_id": "peking/anyreward/anyreward", "name": "AnyRewardModel", "maker": "peking", "family": "peking/anyreward", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "AnyRewardModel is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7605, "dossier_id": "peking/beaver/beaver-7b-unified-cost", "name": "Beaver-7B unified cost", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Beaver-7B unified cost is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7606, "dossier_id": "peking/beaver/beaver-7b-unified-reward", "name": "Beaver-7B unified reward", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Beaver-7B unified reward is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7607, "dossier_id": "peking/beaver/beaver-7b-v1-0", "name": "Beaver-7B v1.0", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Beaver-7B v1.0 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7608, "dossier_id": "peking/beaver/beaver-7b-v1-0-cost", "name": "Beaver-7B v1.0 cost", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7609, "dossier_id": "peking/beaver/beaver-7b-v1-0-reward", "name": "Beaver-7B v1.0 reward", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7610, "dossier_id": "peking/beaver/beaver-7b-v2-0", "name": "Beaver-7B v2.0", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Beaver-7B v2.0 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7611, "dossier_id": "peking/beaver/beaver-7b-v2-0-cost", "name": "Beaver-7B v2.0 cost", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7612, "dossier_id": "peking/beaver/beaver-7b-v2-0-reward", "name": "Beaver-7B v2.0 reward", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7613, "dossier_id": "peking/beaver/beaver-7b-v3-0", "name": "Beaver-7B v3.0", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Beaver-7B v3.0 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7614, "dossier_id": "peking/beaver/beaver-7b-v3-0-cost", "name": "Beaver-7B v3.0 cost", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7615, "dossier_id": "peking/beaver/beaver-7b-v3-0-reward", "name": "Beaver-7B v3.0 reward", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7616, "dossier_id": "peking/beaver/beaver-dam-7b", "name": "Beaver-Dam-7B", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Beaver-Dam-7B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7617, "dossier_id": "peking/beaver/beaver-vision-11b", "name": "Beaver-Vision-11B", "maker": "peking", "family": "peking/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Beaver-Vision-11B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7618, "dossier_id": "peking/chat-univi/chat-univi", "name": "Chat-UniVi", "maker": "peking", "family": "peking/chat-univi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Chat-UniVi is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7619, "dossier_id": "peking/chat-univi/chat-univi-13b", "name": "Chat-UniVi 13B", "maker": "peking", "family": "peking/chat-univi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Chat-UniVi 13B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7620, "dossier_id": "peking/chat-univi/chat-univi-7b-v1-5", "name": "Chat-UniVi 7B v1.5", "maker": "peking", "family": "peking/chat-univi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7621, "dossier_id": "peking/chatlaw/chatlaw-13b", "name": "ChatLaw-13B", "maker": "peking", "family": "peking/chatlaw", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ChatLaw-13B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7622, "dossier_id": "peking/chatlaw/chatlaw-33b", "name": "ChatLaw-33B", "maker": "peking", "family": "peking/chatlaw", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ChatLaw-33B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7623, "dossier_id": "peking/chatlaw/chatlaw2-moe", "name": "ChatLaw2-MoE", "maker": "peking", "family": "peking/chatlaw", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ChatLaw2-MoE is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7624, "dossier_id": "peking/g1/g1-zero-3b", "name": "G1-Zero-3B", "maker": "peking", "family": "peking/g1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "G1-Zero-3B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7625, "dossier_id": "peking/grasp/grasp-4b", "name": "GRASP-4B", "maker": "peking", "family": "peking/grasp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "GRASP-4B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7626, "dossier_id": "peking/grasp/grasp-base-4b", "name": "GRASP-base-4B", "maker": "peking", "family": "peking/grasp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "GRASP-base-4B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7627, "dossier_id": "peking/languagebind/languagebind-audio", "name": "LanguageBind Audio", "maker": "peking", "family": "peking/languagebind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "LanguageBind Audio is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7628, "dossier_id": "peking/languagebind/languagebind-depth", "name": "LanguageBind Depth", "maker": "peking", "family": "peking/languagebind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "LanguageBind Depth is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7629, "dossier_id": "peking/languagebind/languagebind-image", "name": "LanguageBind Image", "maker": "peking", "family": "peking/languagebind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "LanguageBind Image is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7630, "dossier_id": "peking/languagebind/languagebind-thermal", "name": "LanguageBind Thermal", "maker": "peking", "family": "peking/languagebind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "LanguageBind Thermal is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7631, "dossier_id": "peking/languagebind/languagebind-video", "name": "LanguageBind Video", "maker": "peking", "family": "peking/languagebind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "LanguageBind Video is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7632, "dossier_id": "peking/lawyer-llama/lawyer-llama-13b", "name": "Lawyer LLaMA 13B", "maker": "peking", "family": "peking/lawyer-llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Lawyer LLaMA 13B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7633, "dossier_id": "peking/llama2-lrl/llama-2-7b-ben", "name": "Llama-2-7b-ben", "maker": "peking", "family": "peking/llama2-lrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Llama-2-7b-ben is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7634, "dossier_id": "peking/llama2-lrl/llama-2-7b-bod", "name": "Llama-2-7b-bod", "maker": "peking", "family": "peking/llama2-lrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Llama-2-7b-bod is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7635, "dossier_id": "peking/llama2-lrl/llama-2-7b-mon", "name": "Llama-2-7b-mon", "maker": "peking", "family": "peking/llama2-lrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Llama-2-7b-mon is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7636, "dossier_id": "peking/llama2-lrl/llama-2-7b-tam", "name": "Llama-2-7b-tam", "maker": "peking", "family": "peking/llama2-lrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Llama-2-7b-tam is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7637, "dossier_id": "peking/llama2-lrl/llama-2-7b-tel", "name": "Llama-2-7b-tel", "maker": "peking", "family": "peking/llama2-lrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Llama-2-7b-tel is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7638, "dossier_id": "peking/llama2-lrl/llama-2-7b-uig", "name": "Llama-2-7b-uig", "maker": "peking", "family": "peking/llama2-lrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Llama-2-7b-uig is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7639, "dossier_id": "peking/lrl-cpt/gemma-3-4b-bo-cpt", "name": "gemma-3-4b-bo-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7640, "dossier_id": "peking/lrl-cpt/gemma-3-4b-kk-cpt", "name": "gemma-3-4b-kk-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7641, "dossier_id": "peking/lrl-cpt/gemma-3-4b-mn-cpt", "name": "gemma-3-4b-mn-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7642, "dossier_id": "peking/lrl-cpt/gemma-3-4b-ug-cpt", "name": "gemma-3-4b-ug-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7643, "dossier_id": "peking/lrl-cpt/qwen2.5-1.5b-bo-cpt", "name": "Qwen2.5-1.5B-bo-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7644, "dossier_id": "peking/lrl-cpt/qwen2.5-1.5b-kk-cpt", "name": "Qwen2.5-1.5B-kk-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7645, "dossier_id": "peking/lrl-cpt/qwen2.5-1.5b-mn-cpt", "name": "Qwen2.5-1.5B-mn-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7646, "dossier_id": "peking/lrl-cpt/qwen2.5-1.5b-ug-cpt", "name": "Qwen2.5-1.5B-ug-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7647, "dossier_id": "peking/lrl-cpt/qwen2.5-3b-bo-cpt", "name": "Qwen2.5-3B-bo-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7648, "dossier_id": "peking/lrl-cpt/qwen2.5-3b-kk-cpt", "name": "Qwen2.5-3B-kk-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7649, "dossier_id": "peking/lrl-cpt/qwen2.5-3b-mn-cpt", "name": "Qwen2.5-3B-mn-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7650, "dossier_id": "peking/lrl-cpt/qwen2.5-3b-ug-cpt", "name": "Qwen2.5-3B-ug-cpt", "maker": "peking", "family": "peking/lrl-cpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7651, "dossier_id": "peking/moe-llava/moe-llava-openchat-7b-4e", "name": "MoE-LLaVA OpenChat 7B 4e", "maker": "peking", "family": "peking/moe-llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7652, "dossier_id": "peking/moe-llava/moe-llava-phi2-2-7b-4e", "name": "MoE-LLaVA Phi-2 2.7B 4e", "maker": "peking", "family": "peking/moe-llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7653, "dossier_id": "peking/moe-llava/moe-llava-qwen-1-8b-4e", "name": "MoE-LLaVA Qwen 1.8B 4e", "maker": "peking", "family": "peking/moe-llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7654, "dossier_id": "peking/moe-llava/moe-llava-stablelm-1-6b-4e", "name": "MoE-LLaVA StableLM 1.6B 4e", "maker": "peking", "family": "peking/moe-llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7655, "dossier_id": "peking/open-sora-plan/open-sora-plan", "name": "Open-Sora Plan", "maker": "peking", "family": "peking/open-sora-plan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Open-Sora Plan is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7656, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c013-instruct", "name": "ProgressGym-HistLlama3-70B-C013-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7657, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c013-pretrain", "name": "ProgressGym-HistLlama3-70B-C013-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7658, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c014-instruct", "name": "ProgressGym-HistLlama3-70B-C014-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7659, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c014-pretrain", "name": "ProgressGym-HistLlama3-70B-C014-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7660, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c015-instruct", "name": "ProgressGym-HistLlama3-70B-C015-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7661, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c015-pretrain", "name": "ProgressGym-HistLlama3-70B-C015-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7662, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c016-instruct", "name": "ProgressGym-HistLlama3-70B-C016-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7663, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c016-pretrain", "name": "ProgressGym-HistLlama3-70B-C016-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7664, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c017-instruct", "name": "ProgressGym-HistLlama3-70B-C017-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7665, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c017-pretrain", "name": "ProgressGym-HistLlama3-70B-C017-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7666, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c018-instruct", "name": "ProgressGym-HistLlama3-70B-C018-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7667, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c018-pretrain", "name": "ProgressGym-HistLlama3-70B-C018-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7668, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c019-instruct", "name": "ProgressGym-HistLlama3-70B-C019-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7669, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c019-pretrain", "name": "ProgressGym-HistLlama3-70B-C019-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7670, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c020-instruct", "name": "ProgressGym-HistLlama3-70B-C020-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7671, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c020-pretrain", "name": "ProgressGym-HistLlama3-70B-C020-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7672, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c021-instruct", "name": "ProgressGym-HistLlama3-70B-C021-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7673, "dossier_id": "peking/progressgym/progressgym-histllama3-70b-c021-pretrain", "name": "ProgressGym-HistLlama3-70B-C021-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7674, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c013-instruct", "name": "ProgressGym-HistLlama3-8B-C013-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7675, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c013-pretrain", "name": "ProgressGym-HistLlama3-8B-C013-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7676, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c014-instruct", "name": "ProgressGym-HistLlama3-8B-C014-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7677, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c014-pretrain", "name": "ProgressGym-HistLlama3-8B-C014-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7678, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c015-instruct", "name": "ProgressGym-HistLlama3-8B-C015-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7679, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c015-pretrain", "name": "ProgressGym-HistLlama3-8B-C015-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7680, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c016-instruct", "name": "ProgressGym-HistLlama3-8B-C016-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7681, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c016-pretrain", "name": "ProgressGym-HistLlama3-8B-C016-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7682, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c017-instruct", "name": "ProgressGym-HistLlama3-8B-C017-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7683, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c017-pretrain", "name": "ProgressGym-HistLlama3-8B-C017-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7684, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c018-instruct", "name": "ProgressGym-HistLlama3-8B-C018-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7685, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c018-pretrain", "name": "ProgressGym-HistLlama3-8B-C018-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7686, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c019-instruct", "name": "ProgressGym-HistLlama3-8B-C019-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7687, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c019-pretrain", "name": "ProgressGym-HistLlama3-8B-C019-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7688, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c020-instruct", "name": "ProgressGym-HistLlama3-8B-C020-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7689, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c020-pretrain", "name": "ProgressGym-HistLlama3-8B-C020-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7690, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c021-instruct", "name": "ProgressGym-HistLlama3-8B-C021-instruct", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7691, "dossier_id": "peking/progressgym/progressgym-histllama3-8b-c021-pretrain", "name": "ProgressGym-HistLlama3-8B-C021-pretrain", "maker": "peking", "family": "peking/progressgym", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7692, "dossier_id": "peking/protrix/llama-3-protrix", "name": "Llama-3-ProTrix", "maker": "peking", "family": "peking/protrix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Llama-3-ProTrix is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7693, "dossier_id": "peking/protrix/protrix", "name": "ProTrix", "maker": "peking", "family": "peking/protrix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ProTrix is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7694, "dossier_id": "peking/protrix/protrix-coder", "name": "ProTrix-Coder", "maker": "peking", "family": "peking/protrix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ProTrix-Coder is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7695, "dossier_id": "peking/safe-o1/safe-o1-7b", "name": "Safe-o1", "maker": "peking", "family": "peking/safe-o1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Safe-o1 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7696, "dossier_id": "peking/safe-o1/safe-o1-v-7b", "name": "Safe-o1-V", "maker": "peking", "family": "peking/safe-o1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Safe-o1-V is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7697, "dossier_id": "peking/themis/themis", "name": "Themis", "maker": "peking", "family": "peking/themis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Themis is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7698, "dossier_id": "peking/uniworld/uniworld-v1", "name": "UniWorld-V1", "maker": "peking", "family": "peking/uniworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "UniWorld-V1 is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7699, "dossier_id": "peking/video-llava/video-llava-7b", "name": "Video-LLaVA-7B", "maker": "peking", "family": "peking/video-llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Video-LLaVA-7B is an active model from Peking University. This dossier was collected on 2026-10-15. Confidence is high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7700, "dossier_id": "penn/faithful-summary/bart-faithful-summary-detector", "name": "BART faithful summary detector", "maker": "penn", "family": "penn/faithful-summary", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released BART faithful summary detector. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7701, "dossier_id": "penn/l2d/l2d", "name": "DecompT5 (l2d)", "maker": "penn", "family": "penn/l2d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released DecompT5 (l2d). Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7702, "dossier_id": "penn/l2d/l2d-decomp", "name": "DecompT5 decomposition", "maker": "penn", "family": "penn/l2d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released DecompT5 decomposition. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7703, "dossier_id": "penn/l2d/l2d-entail", "name": "DecompEntail entailment", "maker": "penn", "family": "penn/l2d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released DecompEntail entailment. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7704, "dossier_id": "penn/percival/percival-v1", "name": "Percival v1", "maker": "penn", "family": "penn/percival", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released Percival v1. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7705, "dossier_id": "penn/ricl/ricl-pi0-fast-droid", "name": "RICL-pi0-FAST-DROID", "maker": "penn", "family": "penn/ricl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released RICL-pi0-FAST-DROID. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7706, "dossier_id": "penn/temporal-predictor/roberta-temporal-predictor", "name": "RoBERTa temporal predictor", "maker": "penn", "family": "penn/temporal-predictor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released RoBERTa temporal predictor. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7707, "dossier_id": "penn/zeroshotwiki/zeroshotwiki", "name": "ZeroShotWiki", "maker": "penn", "family": "penn/zeroshotwiki", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Pennsylvania released ZeroShotWiki. Status on 2026-10-15: active. Confidence high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7708, "dossier_id": "perplexity/decider/pplx-decider-v1-27b", "name": "pplx-decider-v1-27b", "maker": "perplexity", "family": "perplexity/decider", "context_length": null, "prompt_price_per_m": 0.02, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-decider-v1-27b`. Status: deprecated. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7709, "dossier_id": "perplexity/embed/pplx-embed-context-v1-0-6b", "name": "pplx-embed-context-v1-0.6b", "maker": "perplexity", "family": "perplexity/embed", "context_length": null, "prompt_price_per_m": 0.008, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-embed-context-v1-0.6b`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7710, "dossier_id": "perplexity/embed/pplx-embed-context-v1-4b", "name": "pplx-embed-context-v1-4b", "maker": "perplexity", "family": "perplexity/embed", "context_length": null, "prompt_price_per_m": 0.05, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-embed-context-v1-4b`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7711, "dossier_id": "perplexity/embed/pplx-embed-v1-0-6b", "name": "pplx-embed-v1-0.6b", "maker": "perplexity", "family": "perplexity/embed", "context_length": null, "prompt_price_per_m": 0.004, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-embed-v1-0.6b`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7712, "dossier_id": "perplexity/embed/pplx-embed-v1-4b", "name": "pplx-embed-v1-4b", "maker": "perplexity", "family": "perplexity/embed", "context_length": null, "prompt_price_per_m": 0.03, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-embed-v1-4b`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7713, "dossier_id": "perplexity/embed/pplx-embed-v1-late-0-6b", "name": "pplx-embed-v1-late-0.6b", "maker": "perplexity", "family": "perplexity/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-embed-v1-late-0.6b`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7714, "dossier_id": "perplexity/embed/pplx-embed-v2-context-9b-preview", "name": "pplx-embed-v2-context-9b-preview", "maker": "perplexity", "family": "perplexity/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-embed-v2-context-9b-preview`. Status: preview. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7715, "dossier_id": "perplexity/embed/pplx-embed-v2-late-0-6b", "name": "pplx-embed-v2-late-0.6b", "maker": "perplexity", "family": "perplexity/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-embed-v2-late-0.6b`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7716, "dossier_id": "perplexity/embed/pplx-embed-v2-late-9b", "name": "pplx-embed-v2-late-9b", "maker": "perplexity", "family": "perplexity/embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-embed-v2-late-9b`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7717, "dossier_id": "perplexity/pplx/pplx-70b-chat", "name": "pplx-70b-chat", "maker": "perplexity", "family": "perplexity/pplx", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-70b-chat`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7718, "dossier_id": "perplexity/pplx/pplx-70b-online", "name": "pplx-70b-online", "maker": "perplexity", "family": "perplexity/pplx", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-70b-online`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7719, "dossier_id": "perplexity/pplx/pplx-7b-chat", "name": "pplx-7b-chat", "maker": "perplexity", "family": "perplexity/pplx", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-7b-chat`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7720, "dossier_id": "perplexity/pplx/pplx-7b-online", "name": "pplx-7b-online", "maker": "perplexity", "family": "perplexity/pplx", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `pplx-7b-online`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7721, "dossier_id": "perplexity/r1/r1-1776", "name": "R1 1776", "maker": "perplexity", "family": "perplexity/r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `r1-1776`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7722, "dossier_id": "perplexity/sonar/llama-3-1-sonar-huge-128k-online", "name": "Llama 3.1 Sonar Huge 128k Online", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3.1-sonar-huge-128k-online`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7723, "dossier_id": "perplexity/sonar/llama-3-1-sonar-large-128k-chat", "name": "Llama 3.1 Sonar Large 128k Chat", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3.1-sonar-large-128k-chat`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7724, "dossier_id": "perplexity/sonar/llama-3-1-sonar-large-128k-online", "name": "Llama 3.1 Sonar Large 128k Online", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3.1-sonar-large-128k-online`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7725, "dossier_id": "perplexity/sonar/llama-3-1-sonar-small-128k-chat", "name": "Llama 3.1 Sonar Small 128k Chat", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3.1-sonar-small-128k-chat`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7726, "dossier_id": "perplexity/sonar/llama-3-1-sonar-small-128k-online", "name": "Llama 3.1 Sonar Small 128k Online", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3.1-sonar-small-128k-online`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7727, "dossier_id": "perplexity/sonar/llama-3-sonar-large-32k-chat", "name": "Llama 3 Sonar Large 32k Chat", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3-sonar-large-32k-chat`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7728, "dossier_id": "perplexity/sonar/llama-3-sonar-large-32k-online", "name": "Llama 3 Sonar Large 32k Online", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3-sonar-large-32k-online`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7729, "dossier_id": "perplexity/sonar/llama-3-sonar-small-32k-chat", "name": "Llama 3 Sonar Small 32k Chat", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3-sonar-small-32k-chat`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7730, "dossier_id": "perplexity/sonar/llama-3-sonar-small-32k-online", "name": "Llama 3 Sonar Small 32k Online", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `llama-3-sonar-small-32k-online`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7731, "dossier_id": "perplexity/sonar/sonar-2", "name": "Sonar 2", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `sonar-2`. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7732, "dossier_id": "perplexity/sonar/sonar-reasoning", "name": "Sonar Reasoning", "maker": "perplexity", "family": "perplexity/sonar", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": 5.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Perplexity model `sonar-reasoning`. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7733, "dossier_id": "phrase/next-genmt/next-genmt", "name": "Phrase Next GenMT", "maker": "phrase", "family": "phrase/next-genmt", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7734, "dossier_id": "phrase/nextmt/custom-nextmt", "name": "Phrase Custom NextMT", "maker": "phrase", "family": "phrase/nextmt", "context_length": null, "prompt_price_per_m": 0.5, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7735, "dossier_id": "phrase/nextmt/nextmt", "name": "Phrase NextMT", "maker": "phrase", "family": "phrase/nextmt", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7736, "dossier_id": "phrase/phoenix/phoenix", "name": "Phoenix", "maker": "phrase", "family": "phrase/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7737, "dossier_id": "phrase/translation-agent/ai-translation-agent", "name": "Phrase AI Translation Agent", "maker": "phrase", "family": "phrase/translation-agent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.25, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7738, "dossier_id": "pika/pika/pika-1-0", "name": "Pika 1.0", "maker": "pika", "family": "pika/pika", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7739, "dossier_id": "pika/pika/pika-1-5", "name": "Pika 1.5", "maker": "pika", "family": "pika/pika", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7740, "dossier_id": "pika/pika/pika-2-0", "name": "Pika 2.0", "maker": "pika", "family": "pika/pika", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7741, "dossier_id": "pika/pika/pika-2-1", "name": "Pika 2.1", "maker": "pika", "family": "pika/pika", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.4, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7742, "dossier_id": "pika/pika/pika-2-2", "name": "Pika 2.2", "maker": "pika", "family": "pika/pika", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7743, "dossier_id": "pika/pika/pika-2-5", "name": "Pika 2.5", "maker": "pika", "family": "pika/pika", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7744, "dossier_id": "pika/pika/pika-2-turbo", "name": "Pika 2 Turbo", "maker": "pika", "family": "pika/pika", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.2, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7745, "dossier_id": "pika/pikadditions/pikadditions", "name": "Pikadditions", "maker": "pika", "family": "pika/pikadditions", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7746, "dossier_id": "pika/pikaffects/pikaffects", "name": "Pikaffects", "maker": "pika", "family": "pika/pikaffects", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7747, "dossier_id": "pika/pikaframes/pikaframes", "name": "Pikaframes", "maker": "pika", "family": "pika/pikaframes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7748, "dossier_id": "pika/pikascenes/pikascenes", "name": "Pikascenes", "maker": "pika", "family": "pika/pikascenes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7749, "dossier_id": "pika/pikastream/pikaformance", "name": "Pikaformance", "maker": "pika", "family": "pika/pikastream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7750, "dossier_id": "pika/pikastream/pikastream-1-0", "name": "PikaStream 1.0", "maker": "pika", "family": "pika/pikastream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7751, "dossier_id": "pika/pikastream/pikastream-2-0", "name": "PikaStream 2.0", "maker": "pika", "family": "pika/pikastream", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7752, "dossier_id": "pika/pikaswaps/pikaswaps", "name": "Pikaswaps", "maker": "pika", "family": "pika/pikaswaps", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7753, "dossier_id": "pika/tools/dynamic-captions", "name": "Pika Dynamic Captions", "maker": "pika", "family": "pika/tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.0004, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7754, "dossier_id": "pika/tools/video-compose", "name": "Pika Video Compose", "maker": "pika", "family": "pika/tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.0002, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7755, "dossier_id": "pika/tools/video-merge", "name": "Pika Video Merge", "maker": "pika", "family": "pika/tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.0002, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7756, "dossier_id": "pitt/keyphrase/copyrnn", "name": "CopyRNN", "maker": "pitt", "family": "pitt/keyphrase", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7757, "dossier_id": "pixverse/c/c1", "name": "PixVerse C1", "maker": "pixverse", "family": "pixverse/c", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7758, "dossier_id": "pixverse/r/r1", "name": "PixVerse R1", "maker": "pixverse", "family": "pixverse/r", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7759, "dossier_id": "pixverse/r/r2", "name": "PixVerse R2", "maker": "pixverse", "family": "pixverse/r", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7760, "dossier_id": "pixverse/v/v1", "name": "PixVerse V1", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7761, "dossier_id": "pixverse/v/v2", "name": "PixVerse V2", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7762, "dossier_id": "pixverse/v/v2-5", "name": "PixVerse V2.5", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7763, "dossier_id": "pixverse/v/v3", "name": "PixVerse V3", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7764, "dossier_id": "pixverse/v/v3-5", "name": "PixVerse V3.5", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7765, "dossier_id": "pixverse/v/v4", "name": "PixVerse V4", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7766, "dossier_id": "pixverse/v/v4-5", "name": "PixVerse V4.5", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7767, "dossier_id": "pixverse/v/v5", "name": "PixVerse V5", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7768, "dossier_id": "pixverse/v/v5-5", "name": "PixVerse V5.5", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7769, "dossier_id": "pixverse/v/v5-6", "name": "PixVerse V5.6", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7770, "dossier_id": "pixverse/v/v6", "name": "PixVerse V6", "maker": "pixverse", "family": "pixverse/v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7771, "dossier_id": "playground/playground-v1/playground-v1", "name": "Playground v1", "maker": "playground", "family": "playground/playground-v1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7772, "dossier_id": "playground/playground-v2/playground-v2-1024px-aesthetic", "name": "Playground v2 1024px Aesthetic", "maker": "playground", "family": "playground/playground-v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7773, "dossier_id": "playground/playground-v2/playground-v2-256px-base", "name": "Playground v2 256px Base", "maker": "playground", "family": "playground/playground-v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7774, "dossier_id": "playground/playground-v2/playground-v2-512px-base", "name": "Playground v2 512px Base", "maker": "playground", "family": "playground/playground-v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7775, "dossier_id": "playground/playground-v2-5/photorealism", "name": "Photorealism", "maker": "playground", "family": "playground/playground-v2-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7776, "dossier_id": "playground/playground-v2-5/playground-v2-5-1024px-aesthetic", "name": "Playground v2.5 1024px Aesthetic", "maker": "playground", "family": "playground/playground-v2-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7777, "dossier_id": "playground/playground-v3/playground-v3", "name": "Playground v3", "maker": "playground", "family": "playground/playground-v3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7778, "dossier_id": "playht/dialog/playdialog", "name": "PlayDialog", "maker": "playht", "family": "playht/dialog", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7779, "dossier_id": "playht/dialog/playdialog-arabic", "name": "PlayDialog Arabic", "maker": "playht", "family": "playht/dialog", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7780, "dossier_id": "playht/dialog/playdialog-multilingual", "name": "PlayDialog Multilingual", "maker": "playht", "family": "playht/dialog", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7781, "dossier_id": "playht/dialog/playdialog-turbo", "name": "PlayDialog Turbo", "maker": "playht", "family": "playht/dialog", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 50.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7782, "dossier_id": "playht/parrot/parrot", "name": "Parrot", "maker": "playht", "family": "playht/parrot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7783, "dossier_id": "playht/peregrine/peregrine", "name": "Peregrine", "maker": "playht", "family": "playht/peregrine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7784, "dossier_id": "playht/play/play3-0-mini", "name": "Play 3.0 Mini", "maker": "playht", "family": "playht/play", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7785, "dossier_id": "playht/playht/playht1-0", "name": "PlayHT 1.0", "maker": "playht", "family": "playht/playht", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7786, "dossier_id": "playht/playht/playht2-0", "name": "PlayHT 2.0", "maker": "playht", "family": "playht/playht", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7787, "dossier_id": "playht/playht/playht2-0-turbo", "name": "PlayHT 2.0 Turbo", "maker": "playht", "family": "playht/playht", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7788, "dossier_id": "polimi/crt/crt-libero", "name": "CRT LIBERO", "maker": "polimi", "family": "polimi/crt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Politecnico di Milano AIRLab checkpoint CRT LIBERO (crt-libero). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7789, "dossier_id": "polimi/crt/crt-metaworld", "name": "CRT Meta-World", "maker": "polimi", "family": "polimi/crt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Politecnico di Milano AIRLab checkpoint CRT Meta-World (crt-metaworld). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7790, "dossier_id": "polimi/cupid/cupid", "name": "CUPID", "maker": "polimi", "family": "polimi/cupid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Politecnico di Milano ISPL model CUPID (cupid). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7791, "dossier_id": "polimi/larsnet/larsnet", "name": "LarsNet", "maker": "polimi", "family": "polimi/larsnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "summary_intro": "Politecnico di Milano ISPL model LarsNet (larsnet). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7792, "dossier_id": "polimi/motif/motif", "name": "MOTIF", "maker": "polimi", "family": "polimi/motif", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Politecnico di Milano ISPL model MOTIF (motif). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7793, "dossier_id": "polimi/parcnet/parcnet", "name": "PARCnet", "maker": "polimi", "family": "polimi/parcnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Politecnico di Milano ISPL model PARCnet (parcnet). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7794, "dossier_id": "polimi/parcnet/parcnet-is2", "name": "PARCnet-IS2", "maker": "polimi", "family": "polimi/parcnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Politecnico di Milano ISPL model PARCnet-IS2 (parcnet-is2). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7795, "dossier_id": "polito/astroloc/astroloc", "name": "AstroLoc", "maker": "polito", "family": "polito/astroloc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7796, "dossier_id": "polito/cosplace/cosplace", "name": "CosPlace", "maker": "polito", "family": "polito/cosplace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7797, "dossier_id": "polito/earthloc/earthloc", "name": "EarthLoc", "maker": "polito", "family": "polito/earthloc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7798, "dossier_id": "polito/eigenplaces/eigenplaces", "name": "EigenPlaces", "maker": "polito", "family": "polito/eigenplaces", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7799, "dossier_id": "polito/megaloc/megaloc", "name": "MegaLoc", "maker": "polito", "family": "polito/megaloc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Politecnico di Torino visual place recognition model MegaLoc (megaloc), by Gabriele Berton and Carlo Masone. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7800, "dossier_id": "polytechnique/cosyvoice/cosyvoice2-0-5b-eu", "name": "CosyVoice2-0.5B-EU", "maker": "polytechnique", "family": "polytechnique/cosyvoice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7801, "dossier_id": "polytechnique/ssml/ssml-breaks2ssml-fr-lora", "name": "SSML Breaks-to-SSML French LoRA", "maker": "polytechnique", "family": "polytechnique/ssml", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7802, "dossier_id": "polytechnique/wavlm-vocoder/wavlm-vocoder-french", "name": "WavLM Vocoder French", "maker": "polytechnique", "family": "polytechnique/wavlm-vocoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7803, "dossier_id": "polyu/depthmaster/depthmaster", "name": "DepthMaster", "maker": "polyu", "family": "polyu/depthmaster", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7804, "dossier_id": "polyu/pixrestore/pixrestore-b", "name": "PixRestore-B", "maker": "polyu", "family": "polyu/pixrestore", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7805, "dossier_id": "polyu/pixrestore/pixrestore-l", "name": "PixRestore-L", "maker": "polyu", "family": "polyu/pixrestore", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7806, "dossier_id": "polyu/pixrestore/pixrestore-s", "name": "PixRestore-S", "maker": "polyu", "family": "polyu/pixrestore", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7807, "dossier_id": "polyu/pixrestore/pixrestore-xl", "name": "PixRestore-XL", "maker": "polyu", "family": "polyu/pixrestore", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7808, "dossier_id": "polyu/pvd/pvd", "name": "PVD", "maker": "polyu", "family": "polyu/pvd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7809, "dossier_id": "polyu/stanza-yue/stanza-yue-electra-large", "name": "Cantonese Stanza ELECTRA dependency parser", "maker": "polyu", "family": "polyu/stanza-yue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7810, "dossier_id": "polyu/tvedit/tvedit-qwen", "name": "TV-Edit-Qwen", "maker": "polyu", "family": "polyu/tvedit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7811, "dossier_id": "poolside/laguna/laguna-s-2-1-base", "name": "Laguna S 2.1 base", "maker": "poolside", "family": "poolside/laguna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7812, "dossier_id": "poolside/laguna/laguna-s-2-1-dflash", "name": "Laguna S 2.1 DFlash", "maker": "poolside", "family": "poolside/laguna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7813, "dossier_id": "poolside/laguna/laguna-xs-2-1-dflash", "name": "Laguna XS 2.1 DFlash", "maker": "poolside", "family": "poolside/laguna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7814, "dossier_id": "poolside/laguna/laguna-xs-2-dflash", "name": "Laguna XS.2 DFlash", "maker": "poolside", "family": "poolside/laguna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7815, "dossier_id": "poolside/malibu/malibu-2-0", "name": "Malibu 2.0", "maker": "poolside", "family": "poolside/malibu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7816, "dossier_id": "poolside/point/point", "name": "Point", "maker": "poolside", "family": "poolside/point", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7817, "dossier_id": "porto/tei2go/tei2go-de", "name": "TEI2GO German", "maker": "porto", "family": "porto/tei2go", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7818, "dossier_id": "porto/tei2go/tei2go-en", "name": "TEI2GO English", "maker": "porto", "family": "porto/tei2go", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7819, "dossier_id": "porto/tei2go/tei2go-es", "name": "TEI2GO Spanish", "maker": "porto", "family": "porto/tei2go", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7820, "dossier_id": "porto/tei2go/tei2go-fr", "name": "TEI2GO French", "maker": "porto", "family": "porto/tei2go", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7821, "dossier_id": "porto/tei2go/tei2go-it", "name": "TEI2GO Italian", "maker": "porto", "family": "porto/tei2go", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7822, "dossier_id": "porto/tei2go/tei2go-pt", "name": "TEI2GO Portuguese", "maker": "porto", "family": "porto/tei2go", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7823, "dossier_id": "postech/contragan/contragan", "name": "ContraGAN", "maker": "postech", "family": "postech/contragan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7824, "dossier_id": "postech/da-cmtl/da-cmtl", "name": "DA-CMTL", "maker": "postech", "family": "postech/da-cmtl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7825, "dossier_id": "postech/gigagan/gigagan", "name": "GigaGAN", "maker": "postech", "family": "postech/gigagan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7826, "dossier_id": "postech/reacgan/reacgan", "name": "ReACGAN", "maker": "postech", "family": "postech/reacgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7827, "dossier_id": "postech/vehme/vehme", "name": "VEHME", "maker": "postech", "family": "postech/vehme", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7828, "dossier_id": "preferred-networks/plamo/plamo-100b", "name": "PLaMo-100B-Pretrained", "maker": "preferred-networks", "family": "preferred-networks/plamo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7829, "dossier_id": "preferred-networks/plamo-2/plamo-2-1-prime", "name": "PLaMo 2.1 Prime", "maker": "preferred-networks", "family": "preferred-networks/plamo-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tool-using Japanese business agents that choose databases and external systems from the user request, including multi-tool calls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7830, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7831, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese and English visual question answering, referring expression grounding, task analysis, and anomaly detection.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7832, "dossier_id": "preferred-networks/plamo-3/plamo-3-610m-fin-instruct", "name": "PLaMo 3 610M Fin Instruct", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7833, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7834, "dossier_id": "preferred-networks/plamo-3/plamo-3-nict-2b-base", "name": "PLaMo 3 NICT 2B Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7835, "dossier_id": "preferred-networks/plamo-3/plamo-3-nict-31b-base", "name": "PLaMo 3 NICT 31B Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7836, "dossier_id": "preferred-networks/plamo-3/plamo-3-nict-8b-base", "name": "PLaMo 3 NICT 8B Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7837, "dossier_id": "preferred-networks/plamo-fin/plamo-fin-base", "name": "PLaMo Fin Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-fin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7838, "dossier_id": "preferred-networks/plamo-semground/plamo-semground", "name": "PLaMo-SemGround", "maker": "preferred-networks", "family": "preferred-networks/plamo-semground", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7839, "dossier_id": "preferred-networks/plamo-translate/plamo-2-translate", "name": "PLaMo 2 Translate", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-Japanese translation, including local and on-premises deployment. The card says it is not instruction-tuned for general chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7840, "dossier_id": "preferred-networks/plamo-translate/plamo-2-translate-base", "name": "PLaMo 2 Translate Base", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Starting point for translation research. The card points users who want the finished translator at plamo-2-translate.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7841, "dossier_id": "preferred-networks/plamo-translate/plamo-2-translate-eval", "name": "PLaMo 2 Translate Eval", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pick the better of two translations. It is not the translation model used by the PLaMo Translate product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7842, "dossier_id": "preferred-networks/plamo-translate/plamo-3-translate-31b", "name": "plamo-3-translate 31B", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-centered translation across 53 languages in the PLaMo Translate product, including file, browser, and 15-language meeting translation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7843, "dossier_id": "preferred-networks/plamo-translate/plamo-3-translate-8b", "name": "plamo-3-translate 8B", "maker": "preferred-networks", "family": "preferred-networks/plamo-translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-English and multilingual translation research size. The vendor compares it with plamo-2-translate 8B and TranslateGemma 27B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7844, "dossier_id": "prime-intellect/intellect/intellect-math", "name": "INTELLECT-MATH", "maker": "prime-intellect", "family": "prime-intellect/intellect", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7845, "dossier_id": "prime-intellect/synthetic/synthetic-1-sft-7b", "name": "SYNTHETIC-1-SFT-7B", "maker": "prime-intellect", "family": "prime-intellect/synthetic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7846, "dossier_id": "princeton/autocompressor/autocompressor-1-3b-30k", "name": "AutoCompressor-1.3b-30k", "maker": "princeton", "family": "princeton/autocompressor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7847, "dossier_id": "princeton/autocompressor/autocompressor-2-7b-30k", "name": "AutoCompressor-2.7b-30k", "maker": "princeton", "family": "princeton/autocompressor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7848, "dossier_id": "princeton/autocompressor/autocompressor-2-7b-6k", "name": "AutoCompressor-2.7b-6k", "maker": "princeton", "family": "princeton/autocompressor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7849, "dossier_id": "princeton/autocompressor/autocompressor-llama-2-7b-6k", "name": "AutoCompressor-Llama-2-7b-6k", "maker": "princeton", "family": "princeton/autocompressor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7850, "dossier_id": "princeton/densephrases/densephrases-multi", "name": "densephrases-multi", "maker": "princeton", "family": "princeton/densephrases", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-domain phrase retrieval. The GitHub table labels these exact-match numbers as before any query-side fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7851, "dossier_id": "princeton/densephrases/densephrases-multi-query-multi", "name": "densephrases-multi-query-multi", "maker": "princeton", "family": "princeton/densephrases", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The GitHub table says this checkpoint is query-fine-tuned on multiple datasets and is the demo model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7852, "dossier_id": "princeton/goedel-prover/goedel-code-prover-8b", "name": "Goedel-Code-Prover-8B", "maker": "princeton", "family": "princeton/goedel-prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7853, "dossier_id": "princeton/goedel-prover/goedel-prover-dpo", "name": "Goedel-Prover-DPO", "maker": "princeton", "family": "princeton/goedel-prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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%.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7854, "dossier_id": "princeton/goedel-prover/goedel-prover-sft", "name": "Goedel-Prover-SFT", "maker": "princeton", "family": "princeton/goedel-prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7855, "dossier_id": "princeton/goedel-prover/goedel-prover-v2-32b", "name": "Goedel-Prover-V2-32B", "maker": "princeton", "family": "princeton/goedel-prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7856, "dossier_id": "princeton/goedel-prover/goedel-prover-v2-8b", "name": "Goedel-Prover-V2-8B", "maker": "princeton", "family": "princeton/goedel-prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7857, "dossier_id": "princeton/i1/i1-1b", "name": "i1-1B", "maker": "princeton", "family": "princeton/i1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7858, "dossier_id": "princeton/i1/i1-3b", "name": "i1-3B", "maker": "princeton", "family": "princeton/i1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7859, "dossier_id": "princeton/mabel/mabel-bert-base-uncased", "name": "mabel-bert-base-uncased", "maker": "princeton", "family": "princeton/mabel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7860, "dossier_id": "princeton/mabel/mabel-bert-large-uncased", "name": "mabel-bert-large-uncased", "maker": "princeton", "family": "princeton/mabel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7861, "dossier_id": "princeton/mabel/mabel-roberta-base", "name": "mabel-roberta-base", "maker": "princeton", "family": "princeton/mabel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7862, "dossier_id": "princeton/mabel/mabel-roberta-large", "name": "mabel-roberta-large", "maker": "princeton", "family": "princeton/mabel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7863, "dossier_id": "princeton/sheared-llama/sheared-llama-1-3B", "name": "Sheared-LLaMA-1.3B", "maker": "princeton", "family": "princeton/sheared-llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7864, "dossier_id": "princeton/sheared-llama/sheared-llama-1-3B-sharegpt", "name": "Sheared-LLaMA-1.3B-ShareGPT", "maker": "princeton", "family": "princeton/sheared-llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7865, "dossier_id": "princeton/sheared-llama/sheared-llama-2-7B", "name": "Sheared-LLaMA-2.7B", "maker": "princeton", "family": "princeton/sheared-llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7866, "dossier_id": "princeton/sheared-llama/sheared-llama-2-7B-sharegpt", "name": "Sheared-LLaMA-2.7B-ShareGPT", "maker": "princeton", "family": "princeton/sheared-llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7867, "dossier_id": "princeton/simcse/sup-simcse-bert-base-uncased", "name": "Supervised SimCSE (BERT-base uncased)", "maker": "princeton", "family": "princeton/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7868, "dossier_id": "princeton/simcse/sup-simcse-bert-large-uncased", "name": "Supervised SimCSE (BERT-large uncased)", "maker": "princeton", "family": "princeton/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7869, "dossier_id": "princeton/simcse/sup-simcse-roberta-base", "name": "Supervised SimCSE (RoBERTa-base)", "maker": "princeton", "family": "princeton/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7870, "dossier_id": "princeton/simcse/sup-simcse-roberta-large", "name": "Supervised SimCSE (RoBERTa-large)", "maker": "princeton", "family": "princeton/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7871, "dossier_id": "princeton/simcse/unsup-simcse-bert-base-uncased", "name": "Unsupervised SimCSE (BERT-base uncased)", "maker": "princeton", "family": "princeton/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7872, "dossier_id": "princeton/simcse/unsup-simcse-bert-large-uncased", "name": "Unsupervised SimCSE (BERT-large uncased)", "maker": "princeton", "family": "princeton/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7873, "dossier_id": "princeton/simcse/unsup-simcse-roberta-base", "name": "Unsupervised SimCSE (RoBERTa-base)", "maker": "princeton", "family": "princeton/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7874, "dossier_id": "princeton/simcse/unsup-simcse-roberta-large", "name": "Unsupervised SimCSE (RoBERTa-large)", "maker": "princeton", "family": "princeton/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7875, "dossier_id": "princeton/simpo/gemma-2-9b-it-simpo", "name": "gemma-2-9b-it-SimPO", "maker": "princeton", "family": "princeton/simpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7876, "dossier_id": "princeton/simpo/llama-3-base-8b-sft-simpo", "name": "Llama-3-Base-8B-SFT-SimPO", "maker": "princeton", "family": "princeton/simpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7877, "dossier_id": "princeton/simpo/llama-3-instruct-8b-simpo", "name": "Llama-3-Instruct-8B-SimPO", "maker": "princeton", "family": "princeton/simpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7878, "dossier_id": "princeton/simpo/llama-3-instruct-8b-simpo-v0-2", "name": "Llama-3-Instruct-8B-SimPO-v0.2", "maker": "princeton", "family": "princeton/simpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7879, "dossier_id": "princeton/simpo/mistral-7b-base-sft-simpo", "name": "Mistral-7B-Base-SFT-SimPO", "maker": "princeton", "family": "princeton/simpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7880, "dossier_id": "princeton/simpo/mistral-7b-instruct-simpo", "name": "Mistral-7B-Instruct-SimPO", "maker": "princeton", "family": "princeton/simpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7881, "dossier_id": "princeton/swe-agent-lm/swe-agent-lm-7b", "name": "SWE-agent-LM-7B", "maker": "princeton", "family": "princeton/swe-agent-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7882, "dossier_id": "princeton/swe-llama/swe-llama-13b", "name": "SWE-Llama-13b", "maker": "princeton", "family": "princeton/swe-llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7883, "dossier_id": "princeton/swe-llama/swe-llama-7b", "name": "SWE-Llama-7b", "maker": "princeton", "family": "princeton/swe-llama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7884, "dossier_id": "psu/beaver/beaver-general", "name": "Beaver-General", "maker": "psu", "family": "psu/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7885, "dossier_id": "psu/beaver/beaver-specific", "name": "Beaver-Specific", "maker": "psu", "family": "psu/beaver", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7886, "dossier_id": "purdue/coda-no/coda-no", "name": "CoDA-NO", "maker": "purdue", "family": "purdue/coda-no", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7887, "dossier_id": "purdue/husformer/husformer", "name": "Husformer", "maker": "purdue", "family": "purdue/husformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7888, "dossier_id": "purdue/rwsa/regression-without-softarg", "name": "Heatmap Regression without Soft-Argmax", "maker": "purdue", "family": "purdue/rwsa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7889, "dossier_id": "qmul/remfx/remfx", "name": "RemFX", "maker": "qmul", "family": "qmul/remfx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7890, "dossier_id": "qmul/wave-u-net/wave-u-net", "name": "Wave-U-Net", "maker": "qmul", "family": "qmul/wave-u-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7891, "dossier_id": "qmul/yourmt3/yourmt3-plus", "name": "YourMT3+", "maker": "qmul", "family": "qmul/yourmt3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7892, "dossier_id": "queens/cardiogan/cardiogan", "name": "CardioGAN", "maker": "queens", "family": "queens/cardiogan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7893, "dossier_id": "queens/objectbox/objectbox", "name": "ObjectBox", "maker": "queens", "family": "queens/objectbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7894, "dossier_id": "radboud/crlm/crlm-survival", "name": "CRLM desmoplastic status and survival prediction", "maker": "radboud", "family": "radboud/crlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7895, "dossier_id": "radboud/crlm/crlm-tissue-segmentation", "name": "CRLM liver tissue segmentation", "maker": "radboud", "family": "radboud/crlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7896, "dossier_id": "radboud/gleason/gleason-grading", "name": "Automated Gleason Grading", "maker": "radboud", "family": "radboud/gleason", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7897, "dossier_id": "radboud/hooknet/hooknet", "name": "HookNet", "maker": "radboud", "family": "radboud/hooknet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7898, "dossier_id": "radboud/renal-net/renal-net", "name": "Renal-Net", "maker": "radboud", "family": "radboud/renal-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7899, "dossier_id": "rakuten/rakuten-ai/rakuten-ai-7b-chat-onnx", "name": "Rakuten AI 7B Chat ONNX", "maker": "rakuten", "family": "rakuten/rakuten-ai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7900, "dossier_id": "reading/hydra-lstm/hydra-lstm", "name": "Hydra-LSTM", "maker": "reading", "family": "reading/hydra-lstm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7901, "dossier_id": "recraft/v2/recraft-v2", "name": "Recraft V2", "maker": "recraft", "family": "recraft/v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.022, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7902, "dossier_id": "recraft/v2/recraft-v2-vector", "name": "Recraft V2 Vector", "maker": "recraft", "family": "recraft/v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.044, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7903, "dossier_id": "recraft/v3/recraft-v3", "name": "Recraft V3", "maker": "recraft", "family": "recraft/v3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7904, "dossier_id": "recraft/v3/recraft-v3-vector", "name": "Recraft V3 Vector", "maker": "recraft", "family": "recraft/v3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SVG variant of Recraft V3. Default curated style is Vector art. Same edit endpoints as raster V3.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7905, "dossier_id": "recraft/v4/recraft-v4", "name": "Recraft V4", "maker": "recraft", "family": "recraft/v4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7906, "dossier_id": "recraft/v4/recraft-v4-pro", "name": "Recraft V4 Pro", "maker": "recraft", "family": "recraft/v4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.25, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7907, "dossier_id": "recraft/v4/recraft-v4-pro-vector", "name": "Recraft V4 Pro Vector", "maker": "recraft", "family": "recraft/v4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.3, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-detail SVG variant of Recraft V4.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7908, "dossier_id": "recraft/v4/recraft-v4-vector", "name": "Recraft V4 Vector", "maker": "recraft", "family": "recraft/v4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Production-grade SVG variant of Recraft V4.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7909, "dossier_id": "recraft/v4-1/recraft-v4-1", "name": "Recraft V4.1", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.035, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Default Recraft API model for photorealism, illustration, and short prompts. Raster output around 1K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7910, "dossier_id": "recraft/v4-1/recraft-v4-1-flash", "name": "Recraft V4.1 Flash", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.007, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7911, "dossier_id": "recraft/v4-1/recraft-v4-1-pro", "name": "Recraft V4.1 Pro", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.21, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7912, "dossier_id": "recraft/v4-1/recraft-v4-1-pro-vector", "name": "Recraft V4.1 Pro Vector", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.3, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7913, "dossier_id": "recraft/v4-1/recraft-v4-1-utility", "name": "Recraft V4.1 Utility", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.035, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predictable V4.1 raster line: flat lighting, front-facing composition, simple scenes for mockups and product listings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7914, "dossier_id": "recraft/v4-1/recraft-v4-1-utility-pro", "name": "Recraft V4.1 Utility Pro", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.21, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "2K Utility raster model for predictable product and mockup images at higher resolution.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7915, "dossier_id": "recraft/v4-1/recraft-v4-1-utility-pro-vector", "name": "Recraft V4.1 Utility Pro Vector", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.3, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-detail SVG Utility variant for predictable vector assets.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7916, "dossier_id": "recraft/v4-1/recraft-v4-1-utility-vector", "name": "Recraft V4.1 Utility Vector", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SVG Utility variant: simple, predictable vector output rather than the main line's artistic point of view.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7917, "dossier_id": "recraft/v4-1/recraft-v4-1-vector", "name": "Recraft V4.1 Vector", "maker": "recraft", "family": "recraft/v4-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SVG variant of Recraft V4.1 for logos, typography, and illustration.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7918, "dossier_id": "recraft/v4-styles/recraft-v4-styles", "name": "Recraft V4 Styles", "maker": "recraft", "family": "recraft/v4-styles", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.035, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Dedicated style-match raster model. A style from 1 to 10 reference images is required. No training step.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7919, "dossier_id": "recraft/v4-styles/recraft-v4-styles-pro", "name": "Recraft V4 Styles Pro", "maker": "recraft", "family": "recraft/v4-styles", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.1, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "2K style-match raster model. A style is required. Pro here is cheaper than V4.1 Pro.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7920, "dossier_id": "recraft/v4-styles/recraft-v4-styles-pro-vector", "name": "Recraft V4 Styles Pro Vector", "maker": "recraft", "family": "recraft/v4-styles", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.12, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-resolution SVG style-match model. A style is required.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7921, "dossier_id": "recraft/v4-styles/recraft-v4-styles-vector", "name": "Recraft V4 Styles Vector", "maker": "recraft", "family": "recraft/v4-styles", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SVG variant of V4 Styles. A style is required. Vector price is lower than other V4 vector models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7922, "dossier_id": "reka/core/reka-core", "name": "Reka Core", "maker": "reka", "family": "reka/core", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": 25.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7923, "dossier_id": "reka/edge/reka-edge", "name": "Reka Edge (2024)", "maker": "reka", "family": "reka/edge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7924, "dossier_id": "reka/edge/reka-edgeq", "name": "Reka EdgeQ", "maker": "reka", "family": "reka/edge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7925, "dossier_id": "reka/flash/reka-flash", "name": "Reka Flash", "maker": "reka", "family": "reka/flash", "context_length": null, "prompt_price_per_m": 0.8, "completion_price_per_m": 2.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7926, "dossier_id": "reka/flash/reka-flash-3-1", "name": "Reka Flash 3.1", "maker": "reka", "family": "reka/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7927, "dossier_id": "reka/flash/reka-flash-3-1-q3-k-s", "name": "Reka Flash 3.1 Q3_K_S", "maker": "reka", "family": "reka/flash", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7928, "dossier_id": "reka/rho/rho-1", "name": "Rho-1", "maker": "reka", "family": "reka/rho", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7929, "dossier_id": "reka/ridm/reka-inverse-dynamics-model", "name": "Reka Inverse Dynamics Model", "maker": "reka", "family": "reka/ridm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7930, "dossier_id": "reka/spark/reka-spark", "name": "Reka Spark", "maker": "reka", "family": "reka/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7931, "dossier_id": "reka/speech/reka-speech", "name": "Reka Speech", "maker": "reka", "family": "reka/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7932, "dossier_id": "resemble/chatterbox/chatterbox", "name": "Chatterbox", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7933, "dossier_id": "resemble/chatterbox/chatterbox-es-es", "name": "Chatterbox Multilingual es-es", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7934, "dossier_id": "resemble/chatterbox/chatterbox-es-mx-latam", "name": "Chatterbox Multilingual es-mx-latam", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7935, "dossier_id": "resemble/chatterbox/chatterbox-flash", "name": "Chatterbox Flash", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7936, "dossier_id": "resemble/chatterbox/chatterbox-hi", "name": "Chatterbox Multilingual hi", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7937, "dossier_id": "resemble/chatterbox/chatterbox-multilingual", "name": "Chatterbox Multilingual", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7938, "dossier_id": "resemble/chatterbox/chatterbox-nano", "name": "Chatterbox Nano", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7939, "dossier_id": "resemble/chatterbox/chatterbox-pt-br", "name": "Chatterbox Multilingual pt-br", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7940, "dossier_id": "resemble/chatterbox/chatterbox-pt-pt", "name": "Chatterbox Multilingual pt-pt", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7941, "dossier_id": "resemble/chatterbox/chatterbox-turbo", "name": "Chatterbox Turbo", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7942, "dossier_id": "resemble/chatterbox/chatterbox-zh-cmn", "name": "Chatterbox Multilingual zh-cmn", "maker": "resemble", "family": "resemble/chatterbox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7943, "dossier_id": "resemble/detect/detect-2b", "name": "DETECT-2B", "maker": "resemble", "family": "resemble/detect", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7944, "dossier_id": "resemble/detect/detect-3b-omni", "name": "DETECT-3B Omni", "maker": "resemble", "family": "resemble/detect", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7945, "dossier_id": "resemble/detect/detect-world", "name": "DETECT-World", "maker": "resemble", "family": "resemble/detect", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7946, "dossier_id": "resemble/dramabox/dramabox", "name": "DramaBox", "maker": "resemble", "family": "resemble/dramabox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7947, "dossier_id": "resemble/enhance/resemble-enhance", "name": "Resemble Enhance", "maker": "resemble", "family": "resemble/enhance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7948, "dossier_id": "resemble/perth/perth", "name": "PerTh", "maker": "resemble", "family": "resemble/perth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7949, "dossier_id": "resemble/perth/perth-multimodal", "name": "PerTh Multimodal", "maker": "resemble", "family": "resemble/perth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7950, "dossier_id": "resemble/resemblyzer/resemblyzer", "name": "Resemblyzer", "maker": "resemble", "family": "resemble/resemblyzer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7951, "dossier_id": "resemble/sts/core-sts-v1", "name": "Resemble Core STS v1", "maker": "resemble", "family": "resemble/sts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earlier voice-conversion model for built voices.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7952, "dossier_id": "resemble/sts/core-sts-v2", "name": "Resemble Core STS v2", "maker": "resemble", "family": "resemble/sts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Voice conversion on a built voice. Streaming is marked supported. Dataset requirement is 10 or more minutes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7953, "dossier_id": "resemble/sts/legacy-sts", "name": "Resemble Legacy STS", "maker": "resemble", "family": "resemble/sts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Basic voice conversion on the old platform.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7954, "dossier_id": "resemble/tts/enhanced-tts-v1", "name": "Resemble Enhanced TTS v1", "maker": "resemble", "family": "resemble/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Historical platform TTS. The docs list it for reference only. It is not a valid model field on synthesis requests.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7955, "dossier_id": "resemble/tts/enhanced-tts-v2", "name": "Resemble Enhanced TTS v2", "maker": "resemble", "family": "resemble/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Historical platform TTS. The docs list it for reference only. It is not a valid model field on synthesis requests.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7956, "dossier_id": "resemble/tts/enhanced-tts-v3", "name": "Resemble Enhanced TTS v3", "maker": "resemble", "family": "resemble/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Historical platform TTS. The docs list it for reference only. It is not a valid model field on synthesis requests.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7957, "dossier_id": "resemble/tts/legacy-tts", "name": "Resemble Legacy TTS", "maker": "resemble", "family": "resemble/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Historical platform TTS. The docs list it for reference only. It is not a valid model field on synthesis requests.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7958, "dossier_id": "resemble/tts/resemble-ultra", "name": "Resemble Ultra", "maker": "resemble", "family": "resemble/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7959, "dossier_id": "rev/asr/multilingual-english-spanish", "name": "Multilingual English/Spanish", "maker": "rev", "family": "rev/asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7960, "dossier_id": "rev/asr/v1-asr", "name": "v1 ASR", "maker": "rev", "family": "rev/asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7961, "dossier_id": "rev/reverb/reverb", "name": "Reverb", "maker": "rev", "family": "rev/reverb", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7962, "dossier_id": "rev/reverb/reverb-diarization-v1", "name": "Reverb Diarization v1", "maker": "rev", "family": "rev/reverb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7963, "dossier_id": "rev/reverb/reverb-diarization-v2", "name": "Reverb Diarization v2", "maker": "rev", "family": "rev/reverb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7964, "dossier_id": "rev/reverb/reverb-foreign-language", "name": "Reverb Foreign Language", "maker": "rev", "family": "rev/reverb", "context_length": null, "prompt_price_per_m": 0.3, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7965, "dossier_id": "rev/reverb/reverb-research", "name": "Reverb Research", "maker": "rev", "family": "rev/reverb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7966, "dossier_id": "rev/reverb/reverb-turbo", "name": "Reverb Turbo", "maker": "rev", "family": "rev/reverb", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7967, "dossier_id": "reve/reve/reve-1-5", "name": "Reve v1.5", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7968, "dossier_id": "reve/reve/reve-2-0", "name": "Reve 2.0", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7969, "dossier_id": "reve/reve/reve-2-1", "name": "Reve 2.1", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7970, "dossier_id": "reve/reve/reve-fast-edit", "name": "Reve Fast Edit", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.007, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7971, "dossier_id": "reve/reve/reve-image-1-0", "name": "Reve Image 1.0", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7972, "dossier_id": "reve/reve/reve-layout", "name": "Reve Layout", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7973, "dossier_id": "reve/reve/reve-v1", "name": "Reve V1", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7974, "dossier_id": "reve/reve/reve-v1-1", "name": "Reve V1.1", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-based image editing. Reve's examples were portrait restyling (\"make this look professional, DSLR, medium shot\") and lighting variations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7975, "dossier_id": "reve/reve/reve-v1-1-fast", "name": "Reve V1.1 Fast", "maker": "reve", "family": "reve/reve", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7976, "dossier_id": "reverso/context/reverso-context", "name": "Reverso Context", "maker": "reverso", "family": "reverso/context", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7977, "dossier_id": "reverso/nmt/neural-engine", "name": "Reverso Neural Engine", "maker": "reverso", "family": "reverso/nmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7978, "dossier_id": "rice/ldamp/ldamp", "name": "Learned D-AMP", "maker": "rice", "family": "rice/ldamp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7979, "dossier_id": "rice/prdeep/prdeep", "name": "prDeep", "maker": "rice", "family": "rice/prdeep", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7980, "dossier_id": "rice/robotok/robotok", "name": "RoboTok", "maker": "rice", "family": "rice/robotok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7981, "dossier_id": "rice/shiftaddnet/shiftaddnet", "name": "ShiftAddNet", "maker": "rice", "family": "rice/shiftaddnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7982, "dossier_id": "ricoh/llm/llm-13b", "name": "Ricoh 13B Japanese LLM", "maker": "ricoh", "family": "ricoh/llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7983, "dossier_id": "ricoh/llm/llm-27b", "name": "\u30ea\u30b3\u30fcLLM_27B", "maker": "ricoh", "family": "ricoh/llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7984, "dossier_id": "ricoh/llm/llm-70b", "name": "\u30ea\u30b3\u30fcLLM_70B", "maker": "ricoh", "family": "ricoh/llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7985, "dossier_id": "ricoh/lmm/lmm-27b", "name": "\u30ea\u30b3\u30fcLMM_27B", "maker": "ricoh", "family": "ricoh/lmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7986, "dossier_id": "ricoh/lmm/lmm-32b", "name": "\u30ea\u30b3\u30fcLMM_32B", "maker": "ricoh", "family": "ricoh/lmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7987, "dossier_id": "ricoh/lmm/lmm-70b", "name": "\u30ea\u30b3\u30fcLMM_70B", "maker": "ricoh", "family": "ricoh/lmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7988, "dossier_id": "ricoh/lmm/lmm-8b", "name": "\u30ea\u30b3\u30fcLMM_8B", "maker": "ricoh", "family": "ricoh/lmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7989, "dossier_id": "ricoh/lmm/lmm-9b", "name": "\u30ea\u30b3\u30fcLMM_9B", "maker": "ricoh", "family": "ricoh/lmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7990, "dossier_id": "ricoh/lmm/qwen25-vl-ricoh-32b", "name": "Qwen-2.5-VL-Ricoh-32B-20250918", "maker": "ricoh", "family": "ricoh/lmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7991, "dossier_id": "ricoh/safeguard/llama-ricoh-safeguard", "name": "Llama-Ricoh-SafeGuard", "maker": "ricoh", "family": "ricoh/safeguard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7992, "dossier_id": "rime/arcana/arcana", "name": "Arcana", "maker": "rime", "family": "rime/arcana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7993, "dossier_id": "rime/arcana/arcana-v2", "name": "Arcana v2", "maker": "rime", "family": "rime/arcana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7994, "dossier_id": "rime/arcana/arcana-v3", "name": "Arcana v3", "maker": "rime", "family": "rime/arcana", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7995, "dossier_id": "rime/coda/coda", "name": "Coda", "maker": "rime", "family": "rime/coda", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7996, "dossier_id": "rime/mist/mist", "name": "Mist", "maker": "rime", "family": "rime/mist", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7997, "dossier_id": "rime/mist/mist-v2", "name": "Mist v2", "maker": "rime", "family": "rime/mist", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7998, "dossier_id": "rime/mist/mist-v3", "name": "Mist v3", "maker": "rime", "family": "rime/mist", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 7999, "dossier_id": "rime/rimecaster/rimecaster", "name": "Rimecaster", "maker": "rime", "family": "rime/rimecaster", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8000, "dossier_id": "rinna/bakeneko/qwen2-5-bakeneko-32b-instruct-v2", "name": "Qwen2.5 Bakeneko 32B Instruct v2", "maker": "rinna", "family": "rinna/bakeneko", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Later Bakeneko instruction model. The series table calls it the QwQ Bakeneko merged instruction-tuning model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8001, "dossier_id": "rinna/bilingual-gpt-neox/bilingual-gpt-neox-4b-minigpt4", "name": "Bilingual GPT-NeoX 4B MiniGPT-4", "maker": "rinna", "family": "rinna/bilingual-gpt-neox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-Japanese image conversation model. It answers text prompts about an image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8002, "dossier_id": "rinna/clip/japanese-clip-vit-b-16", "name": "Japanese CLIP ViT-B/16", "maker": "rinna", "family": "rinna/clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese CLIP for image-text embeddings and zero-shot classification.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8003, "dossier_id": "rinna/cloob/japanese-cloob-vit-b-16", "name": "Japanese CLOOB ViT-B/16", "maker": "rinna", "family": "rinna/cloob", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8004, "dossier_id": "rinna/data2vec/japanese-data2vec-audio-base", "name": "Japanese data2vec Audio Base", "maker": "rinna", "family": "rinna/data2vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Self-supervised Japanese data2vec audio encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8005, "dossier_id": "rinna/hubert/japanese-hubert-base", "name": "Japanese HuBERT Base", "maker": "rinna", "family": "rinna/hubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Self-supervised Japanese speech encoder. It is also the audio encoder initialized inside Nue ASR.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8006, "dossier_id": "rinna/hubert/japanese-hubert-large", "name": "Japanese HuBERT Large", "maker": "rinna", "family": "rinna/hubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger Japanese HuBERT speech encoder, released with wav2vec 2.0 Base and data2vec Audio Base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8007, "dossier_id": "rinna/koemotion/koeiromap", "name": "Koeiromap", "maker": "rinna", "family": "rinna/koemotion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "rinna's speaker-map speech synthesizer. It is the voice model inside the Koemotion product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8008, "dossier_id": "rinna/koemotion/koemotion", "name": "Koemotion", "maker": "rinna", "family": "rinna/koemotion", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "rinna's commercial text-to-speech and face-motion service. Version 2.0 added a revised speaker map and streaming.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8009, "dossier_id": "rinna/nue-asr/nue-asr", "name": "Nue ASR", "maker": "rinna", "family": "rinna/nue-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese speech recognizer that maps speech to text with a GPT-style decoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8010, "dossier_id": "rinna/stable-diffusion/japanese-stable-diffusion", "name": "Japanese Stable Diffusion", "maker": "rinna", "family": "rinna/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8011, "dossier_id": "rinna/wav2vec2/japanese-wav2vec2-base", "name": "Japanese wav2vec 2.0 Base", "maker": "rinna", "family": "rinna/wav2vec2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Self-supervised Japanese wav2vec 2.0 speech encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8012, "dossier_id": "rit/ellseg/denseelnet", "name": "DenseElNet", "maker": "rit", "family": "rit/ellseg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8013, "dossier_id": "rit/ritnet/ritnet", "name": "RITnet", "maker": "rit", "family": "rit/ritnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8014, "dossier_id": "rit/scanssd/scanssd", "name": "ScanSSD", "maker": "rit", "family": "rit/scanssd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8015, "dossier_id": "rochester/ave/av-att", "name": "A+V-att", "maker": "rochester", "family": "rochester/ave", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8016, "dossier_id": "rochester/ave/cmm", "name": "AVE cross-modal matching network", "maker": "rochester", "family": "rochester/ave", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8017, "dossier_id": "rochester/avvp/mmil-net", "name": "MMIL-Net", "maker": "rochester", "family": "rochester/avvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8018, "dossier_id": "rochester/dsgpm/dsgpm", "name": "DSGPM", "maker": "rochester", "family": "rochester/dsgpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8019, "dossier_id": "rochester/tdan/tdan", "name": "TDAN", "maker": "rochester", "family": "rochester/tdan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8020, "dossier_id": "rochester/zooming-slow-mo/zooming-slow-mo", "name": "Zooming Slow-Mo", "maker": "rochester", "family": "rochester/zooming-slow-mo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8021, "dossier_id": "ruc/erya/erya-4ft", "name": "Erya4FT", "maker": "ruc", "family": "ruc/erya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8022, "dossier_id": "ruc/live/live-t5-base", "name": "LIVE-T5", "maker": "ruc", "family": "ruc/live", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8023, "dossier_id": "ruc/mvp/mtl-data-to-text", "name": "MTL data-to-text", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8024, "dossier_id": "ruc/mvp/mtl-open-dialog", "name": "MTL open dialog", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8025, "dossier_id": "ruc/mvp/mtl-question-answering", "name": "MTL question answering", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8026, "dossier_id": "ruc/mvp/mtl-question-generation", "name": "MTL question generation", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8027, "dossier_id": "ruc/mvp/mtl-story", "name": "MTL story", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8028, "dossier_id": "ruc/mvp/mtl-summarization", "name": "MTL summarization", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8029, "dossier_id": "ruc/mvp/mtl-task-dialog", "name": "MTL task dialog", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8030, "dossier_id": "ruc/mvp/mvp-data-to-text", "name": "MVP data-to-text", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8031, "dossier_id": "ruc/mvp/mvp-multi-task", "name": "MVP multi-task", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8032, "dossier_id": "ruc/mvp/mvp-open-dialog", "name": "MVP open dialog", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8033, "dossier_id": "ruc/mvp/mvp-question-answering", "name": "MVP question answering", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8034, "dossier_id": "ruc/mvp/mvp-question-generation", "name": "MVP question generation", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8035, "dossier_id": "ruc/mvp/mvp-story", "name": "MVP story", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8036, "dossier_id": "ruc/mvp/mvp-summarization", "name": "MVP summarization", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8037, "dossier_id": "ruc/mvp/mvp-task-dialog", "name": "MVP task dialog", "maker": "ruc", "family": "ruc/mvp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8038, "dossier_id": "ruc/omniatlas/omniatlas-qwen2-5-3b", "name": "OmniAtlas-Qwen2.5-3B", "maker": "ruc", "family": "ruc/omniatlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8039, "dossier_id": "ruc/omniatlas/omniatlas-qwen2-5-7b", "name": "OmniAtlas-Qwen2.5-7B", "maker": "ruc", "family": "ruc/omniatlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8040, "dossier_id": "ruc/omniatlas/omniatlas-qwen3-30b-a3b", "name": "OmniAtlas-Qwen3-30B-A3B", "maker": "ruc", "family": "ruc/omniatlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8041, "dossier_id": "ruc/omnieval/omnieval-hallucination-evaluator", "name": "OmniEval hallucination evaluator", "maker": "ruc", "family": "ruc/omnieval", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8042, "dossier_id": "ruc/omnieval/omnieval-model-evaluator", "name": "OmniEval model evaluator", "maker": "ruc", "family": "ruc/omnieval", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8043, "dossier_id": "ruc/unicorn/unicorn-plus-v1", "name": "Unicorn Plus v1", "maker": "ruc", "family": "ruc/unicorn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8044, "dossier_id": "ruc/yulan/yulan-chat-1-13b", "name": "YuLan-Chat-1-13B", "maker": "ruc", "family": "ruc/yulan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8045, "dossier_id": "ruc/yulan/yulan-chat-1-65b", "name": "YuLan-Chat-1-65B", "maker": "ruc", "family": "ruc/yulan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8046, "dossier_id": "runway/act/act-one", "name": "Act-One", "maker": "runway", "family": "runway/act", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.1, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8047, "dossier_id": "runway/act/act-two", "name": "Act-Two", "maker": "runway", "family": "runway/act", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8048, "dossier_id": "runway/aleph/aleph", "name": "Aleph", "maker": "runway", "family": "runway/aleph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.15, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8049, "dossier_id": "runway/aleph/aleph-2-0", "name": "Aleph 2.0", "maker": "runway", "family": "runway/aleph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.28, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8050, "dossier_id": "runway/enhance/enhance-frame-rate", "name": "Enhance Frame Rate", "maker": "runway", "family": "runway/enhance", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.01, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Frame-rate conversion for generated or uploaded video, aimed at broadcast and editorial delivery rates.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8051, "dossier_id": "runway/frames/frames", "name": "Frames", "maker": "runway", "family": "runway/frames", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8052, "dossier_id": "runway/gen/gen-1", "name": "Gen-1", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8053, "dossier_id": "runway/gen/gen-2", "name": "Gen-2", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8054, "dossier_id": "runway/gen/gen-3-alpha", "name": "Gen-3 Alpha", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.1, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8055, "dossier_id": "runway/gen/gen-3-alpha-turbo", "name": "Gen-3 Alpha Turbo", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8056, "dossier_id": "runway/gen/gen-4", "name": "Gen-4", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.12, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8057, "dossier_id": "runway/gen/gen-4-5", "name": "Gen-4.5", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.12, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8058, "dossier_id": "runway/gen/gen-4-image", "name": "Gen-4 Image", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Runway's primary image generation and editing model, including reference-image consistency for characters, objects, and scenes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8059, "dossier_id": "runway/gen/gen-4-image-turbo", "name": "Gen-4 Image Turbo", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.02, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster, cheaper Gen-4 image model for reference-conditioned generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8060, "dossier_id": "runway/gen/gen-4-turbo", "name": "Gen-4 Turbo", "maker": "runway", "family": "runway/gen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8061, "dossier_id": "runway/gwm/gwm-1", "name": "GWM-1", "maker": "runway", "family": "runway/gwm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8062, "dossier_id": "runway/gwm/gwm-avatars", "name": "GWM Avatars", "maker": "runway", "family": "runway/gwm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.02, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8063, "dossier_id": "runway/gwm/gwm-robotics", "name": "GWM Robotics", "maker": "runway", "family": "runway/gwm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Robotics world model for policy features, synthetic data, and simulated evaluation of robot policies without a physical robot.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8064, "dossier_id": "runway/gwm/gwm-worlds", "name": "GWM Worlds", "maker": "runway", "family": "runway/gwm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8065, "dossier_id": "runway/gwm/gwm-worlds-2", "name": "GWM Worlds 2", "maker": "runway", "family": "runway/gwm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8066, "dossier_id": "runway/ruby/ruby", "name": "Ruby", "maker": "runway", "family": "runway/ruby", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.2, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8067, "dossier_id": "rutgers/dd/rutgers-dd-4source", "name": "Rutgers DD 4-source", "maker": "rutgers", "family": "rutgers/dd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8068, "dossier_id": "rutgers/hermes/hermes-r", "name": "Hermes-R", "maker": "rutgers", "family": "rutgers/hermes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8069, "dossier_id": "rwkv/rwkv-3/pile", "name": "RWKV-3 Pile", "maker": "rwkv", "family": "rwkv/rwkv-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English base language models. The 1.5B card tells readers to move to RWKV-4 1.5B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8070, "dossier_id": "rwkv/rwkv-4/novel", "name": "RWKV-4 Novel", "maker": "rwkv", "family": "rwkv/rwkv-4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-form novel generation. The card distinguishes a general Chinese-English mix from a professional Chinese-novel continuation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8071, "dossier_id": "rwkv/rwkv-4/pile", "name": "RWKV-4 Pile", "maker": "rwkv", "family": "rwkv/rwkv-4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8072, "dossier_id": "rwkv/rwkv-4/raven", "name": "RWKV-4 Raven", "maker": "rwkv", "family": "rwkv/rwkv-4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8073, "dossier_id": "rwkv/rwkv-5/eaglex", "name": "RWKV-5 EagleX", "maker": "rwkv", "family": "rwkv/rwkv-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8074, "dossier_id": "rwkv/rwkv-5/world", "name": "RWKV-5 World (Eagle)", "maker": "rwkv", "family": "rwkv/rwkv-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8075, "dossier_id": "rwkv/rwkv-7/g1", "name": "RWKV-7 G1 (GooseOne)", "maker": "rwkv", "family": "rwkv/rwkv-7", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8076, "dossier_id": "rwkv/rwkv-8/pile", "name": "RWKV-8 Pile", "maker": "rwkv", "family": "rwkv/rwkv-8", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Architecture testing on the Pile. The card does not describe a chat or reasoning product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8077, "dossier_id": "rwth/ditr/ditr", "name": "DITR", "maker": "rwth", "family": "rwth/ditr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8078, "dossier_id": "rwth/donut/donut", "name": "DONUT", "maker": "rwth", "family": "rwth/donut", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8079, "dossier_id": "rwth/interactive4d/interactive4d", "name": "Interactive4D", "maker": "rwth", "family": "rwth/interactive4d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8080, "dossier_id": "rwth/mask3d/mask3d", "name": "Mask3D", "maker": "rwth", "family": "rwth/mask3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mask3D predicts semantic instances directly with transformer queries. The authors contrast it with voting and hand-tuned clustering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8081, "dossier_id": "rwth/mask4former/mask4former", "name": "Mask4Former", "maker": "rwth", "family": "rwth/mask4former", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8082, "dossier_id": "rwth/metrabs/metrabs", "name": "MeTRAbs", "maker": "rwth", "family": "rwth/metrabs", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8083, "dossier_id": "rwth/point2vec/point2vec", "name": "Point2Vec", "maker": "rwth", "family": "rwth/point2vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8084, "dossier_id": "rwth/surge/surge-large", "name": "SurGe Large", "maker": "rwth", "family": "rwth/surge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8085, "dossier_id": "rwth/tarvis/tarvis", "name": "TarViS", "maker": "rwth", "family": "rwth/tarvis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8086, "dossier_id": "rwth/zeyer-ctc/2024-zeyer-ctc-librispeech-spm10k", "name": "2024 Zeyer CTC LibriSpeech SPM-10k", "maker": "rwth", "family": "rwth/zeyer-ctc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8087, "dossier_id": "saarland/afrihubert/afrihubert", "name": "AfriHuBERT", "maker": "saarland", "family": "saarland/afrihubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8088, "dossier_id": "saarland/afrihubert/afrihubert-xlarge", "name": "AfriHuBERT-xlarge", "maker": "saarland", "family": "saarland/afrihubert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8089, "dossier_id": "saarland/afro-xlmr/afro-xlmr-base", "name": "AfroXLMR-base", "maker": "saarland", "family": "saarland/afro-xlmr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8090, "dossier_id": "saarland/afro-xlmr/afro-xlmr-large", "name": "AfroXLMR-large", "maker": "saarland", "family": "saarland/afro-xlmr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8091, "dossier_id": "saarland/afro-xlmr/afro-xlmr-mini", "name": "AfroXLMR-mini", "maker": "saarland", "family": "saarland/afro-xlmr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The smallest 2022 AfroXLMR checkpoint, from MAFT of the distilled XLM-R-miniLM encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8092, "dossier_id": "saarland/afro-xlmr/afro-xlmr-small", "name": "AfroXLMR-small", "maker": "saarland", "family": "saarland/afro-xlmr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8093, "dossier_id": "saarland/mcse/mcse-coco-bert-base-uncased", "name": "MCSE COCO BERT-base", "maker": "saarland", "family": "saarland/mcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8094, "dossier_id": "saarland/mcse/mcse-coco-roberta-base", "name": "MCSE COCO RoBERTa-base", "maker": "saarland", "family": "saarland/mcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8095, "dossier_id": "saarland/mcse/mcse-flickr-bert-base-uncased", "name": "MCSE Flickr BERT-base", "maker": "saarland", "family": "saarland/mcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8096, "dossier_id": "saarland/mcse/mcse-flickr-roberta-base", "name": "MCSE Flickr RoBERTa-base", "maker": "saarland", "family": "saarland/mcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8097, "dossier_id": "saarland/smole/muv2x-simcse-smole-bert", "name": "MUV 2x SimCSE Smole-BERT", "maker": "saarland", "family": "saarland/smole", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8098, "dossier_id": "saarland/smole/siamese-smole-bert-muv-1x", "name": "Siamese Smole-BERT MUV 1x", "maker": "saarland", "family": "saarland/smole", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Siamese contrastive adaptation of Smole-BERT on MUV, released for molecular feature extraction and virtual screening.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8099, "dossier_id": "saarland/smole/simcse-smole-bert-muv-mlm", "name": "SimCSE Smole-BERT MUV MLM", "maker": "saarland", "family": "saarland/smole", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8100, "dossier_id": "saarland/smole/smole-bert-mtr", "name": "Smole-BERT-MTR", "maker": "saarland", "family": "saarland/smole", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The multi-task-regression pretrained BERT in the same SMILES suite, aimed at molecular property features and MUV virtual screening.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8101, "dossier_id": "sakana/ctm/ctm-imagenet", "name": "CTM-ImageNet", "maker": "sakana", "family": "sakana/ctm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "ImageNet classification with a recurrent continuous-thought architecture, for research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8102, "dossier_id": "sakana/ctm/ctm-maze-large", "name": "CTM-Maze-Large", "maker": "sakana", "family": "sakana/ctm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Solve 2D mazes as a sequence of moves, demonstrating CTM on a planning task.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8103, "dossier_id": "sakana/cycleqd/llama-3-8b-instruct-coding-expert", "name": "Llama-3-8B-Instruct-Coding-Expert", "maker": "sakana", "family": "sakana/cycleqd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Coding expert in the CycleQD starting population.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8104, "dossier_id": "sakana/cycleqd/llama-3-8b-instruct-cycleqd-cs", "name": "Llama-3-8B-Instruct-CycleQD-CS", "maker": "sakana", "family": "sakana/cycleqd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "CycleQD computer-science generalist merged from the coding, DB, and OS experts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8105, "dossier_id": "sakana/cycleqd/llama-3-8b-instruct-db-expert", "name": "Llama-3-8B-Instruct-DB-Expert", "maker": "sakana", "family": "sakana/cycleqd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Database expert in the CycleQD starting population.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8106, "dossier_id": "sakana/cycleqd/llama-3-8b-instruct-os-expert", "name": "Llama-3-8B-Instruct-OS-Expert", "maker": "sakana", "family": "sakana/cycleqd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Operating-system expert in the CycleQD starting population.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8107, "dossier_id": "sakana/discopop/discopop-zephyr-7b-gemma", "name": "DiscoPOP-zephyr-7b-gemma", "maker": "sakana", "family": "sakana/discopop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8108, "dossier_id": "sakana/dreamcubed/dreamcubed-2m", "name": "DreamCubed2M", "maker": "sakana", "family": "sakana/dreamcubed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Minecraft world diffusion checkpoint from the Dream-Cubed release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8109, "dossier_id": "sakana/dreamcubed/dreamcubed-cp2", "name": "DreamCubedCP2", "maker": "sakana", "family": "sakana/dreamcubed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Minecraft world diffusion checkpoint from the Dream-Cubed release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8110, "dossier_id": "sakana/dreamcubed/dreamcubed-dp2", "name": "DreamCubedDP2", "maker": "sakana", "family": "sakana/dreamcubed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Minecraft world diffusion checkpoint from the Dream-Cubed release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8111, "dossier_id": "sakana/dreamcubed/dreamcubed-dp4", "name": "DreamCubedDP4", "maker": "sakana", "family": "sakana/dreamcubed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Minecraft world diffusion checkpoint from the Dream-Cubed release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8112, "dossier_id": "sakana/drope/llama-2-7b-hf-drope", "name": "Llama-2-7B-hf-DroPE", "maker": "sakana", "family": "sakana/drope", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research checkpoint for context extension by dropping positional embeddings on Llama 2 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8113, "dossier_id": "sakana/evollm/evollm-jp-a-v1-7b", "name": "EvoLLM-JP-A-v1-7B", "maker": "sakana", "family": "sakana/evollm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Apache-2.0 Japanese merge with a similar job to EvoLLM-JP-v1-7B, swapping WizardMath for Arithmo2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8114, "dossier_id": "sakana/evollm/evollm-jp-v1-10b", "name": "EvoLLM-JP-v1-10B", "maker": "sakana", "family": "sakana/evollm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger experimental Japanese LLM from the same merge project, with the highest MGSM-JA of the three EvoLLM releases.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8115, "dossier_id": "sakana/evollm/evollm-jp-v1-7b", "name": "EvoLLM-JP-v1-7B", "maker": "sakana", "family": "sakana/evollm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Experimental Japanese general LLM with stronger math than its merge parents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8116, "dossier_id": "sakana/evosdxl/evo-nishikie-v1", "name": "Evo-Nishikie-v1", "maker": "sakana", "family": "sakana/evosdxl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Colorize or restyle ukiyo-e and monochrome classical-book images.", "summary_intro": "Evo-Nishikie-v1 is Sakana AI's ukiyo-e colorization model, posted 11 July 2024. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8117, "dossier_id": "sakana/evosdxl/evo-ukiyoe-v1", "name": "Evo-Ukiyoe-v1", "maker": "sakana", "family": "sakana/evosdxl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate ukiyo-e-like images from Japanese prompts, for education and cultural use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8118, "dossier_id": "sakana/evosdxl/evosdxl-jp-v1", "name": "EvoSDXL-JP-v1", "maker": "sakana", "family": "sakana/evosdxl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast Japanese text-to-image generation for education and research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8119, "dossier_id": "sakana/evovlm/evovlm-jp-v1-7b", "name": "EvoVLM-JP-v1-7B", "maker": "sakana", "family": "sakana/evovlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Answer Japanese questions about images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8120, "dossier_id": "sakana/evovlm/llama-3-evovlm-jp-v2", "name": "Llama-3-EvoVLM-JP-v2", "maker": "sakana", "family": "sakana/evovlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese visual chat with interleaved images and text, a later Llama 3 merge rather than a fine-tune of v1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8121, "dossier_id": "sakana/fishmath/gpt-oss-120b-sft-aimo3-fishmath", "name": "gpt-oss-120b SFT AIMO3 FishMath", "maker": "sakana", "family": "sakana/fishmath", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contest-math SFT checkpoint used in Sakana's AIMO3 solution. Not a general chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8122, "dossier_id": "sakana/kame/kame", "name": "KAME", "maker": "sakana", "family": "sakana/kame", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-latency speech conversation with a text LLM supplying knowledge in the background.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8123, "dossier_id": "sakana/karamaru/llama-3-karamaru-v1", "name": "Llama-3-Karamaru-v1", "maker": "sakana", "family": "sakana/karamaru", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Converse in Edo-period Japanese rather than modern Japanese.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8124, "dossier_id": "sakana/metom/metom", "name": "Metom", "maker": "sakana", "family": "sakana/metom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classify kuzushiji characters from images.", "summary_intro": "Metom is Sakana AI's small Apache-2.0 kuzushiji Vision Transformer, posted 20 November 2024. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8125, "dossier_id": "sakana/namazu/llama-3-1-namazu-405b", "name": "Llama-3.1-Namazu-405B", "maker": "sakana", "family": "sakana/namazu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8126, "dossier_id": "sakana/namazu/namazu-deepseek-v3-1-terminus", "name": "Namazu-DeepSeek-V3.1-Terminus", "maker": "sakana", "family": "sakana/namazu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8127, "dossier_id": "sakana/namazu/namazu-gpt-oss-120b", "name": "Namazu-gpt-oss-120B", "maker": "sakana", "family": "sakana/namazu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8128, "dossier_id": "sakana/repo/repo-olmo2-1b-stage2-l5", "name": "RePo-OLMo2-1B-stage2-L5", "maker": "sakana", "family": "sakana/repo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base LM checkpoint showing context re-positioning on OLMo 2 1B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8129, "dossier_id": "sakana/repo/repo-olmo2-7b-stage2-l10", "name": "RePo-OLMo2-7B-stage2-L10", "maker": "sakana", "family": "sakana/repo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "7B base checkpoint for the RePo continual-pretrain experiment.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8130, "dossier_id": "sakana/rlt/rlt-32b", "name": "RLT-32B", "maker": "sakana", "family": "sakana/rlt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger student distilled from the same 7B RLT teacher.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8131, "dossier_id": "sakana/rlt/rlt-7b", "name": "RLT-7B", "maker": "sakana", "family": "sakana/rlt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small reasoning student distilled from a teacher trained to explain, for test-time scaling research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8132, "dossier_id": "sakana/sparselm/sparselm-0-5b", "name": "SparseLM0.5B", "maker": "sakana", "family": "sakana/sparselm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small sparse Llama-style research checkpoints released with Sakana's sparsity kernels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8133, "dossier_id": "sakana/sparselm/sparselm-1-5b", "name": "SparseLM1.5B", "maker": "sakana", "family": "sakana/sparselm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small sparse Llama-style research checkpoints released with Sakana's sparsity kernels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8134, "dossier_id": "sakana/sparselm/sparselm-1b", "name": "SparseLM1B", "maker": "sakana", "family": "sakana/sparselm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small sparse Llama-style research checkpoints released with Sakana's sparsity kernels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8135, "dossier_id": "sakana/sparselm/sparselm-2b", "name": "SparseLM2B", "maker": "sakana", "family": "sakana/sparselm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small sparse Llama-style research checkpoints released with Sakana's sparsity kernels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8136, "dossier_id": "sakana/taid/taid-llm-1-5b", "name": "TAID-LLM-1.5B", "maker": "sakana", "family": "sakana/taid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English compact LM showing TAID distillation from a 72B teacher.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8137, "dossier_id": "sakana/taid/taid-vlm-2b", "name": "TAID-VLM-2B", "maker": "sakana", "family": "sakana/taid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Compact English visual question answering model used to show TAID beyond text.", "summary_intro": "TAID-VLM-2B is Sakana AI's MIT-licensed 2B vision-language TAID student, posted 14 August 2024. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8138, "dossier_id": "salamanca/trecard/trecard-renal-histology-unet", "name": "TRECARD Renal Histology Segmentation U-Net", "maker": "salamanca", "family": "salamanca/trecard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8139, "dossier_id": "salesforce/albef/albef", "name": "ALBEF", "maker": "salesforce", "family": "salesforce/albef", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-text retrieval, VQA, visual entailment, NLVR2, and visual grounding. The README says pretrain and those finetunes are supported.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8140, "dossier_id": "salesforce/alpro/alpro", "name": "ALPRO", "maker": "salesforce", "family": "salesforce/alpro", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-video retrieval on MSRVTT and DiDeMo, and video question answering on MSRVTT and MSVD.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8141, "dossier_id": "salesforce/blip/blip", "name": "BLIP", "maker": "salesforce", "family": "salesforce/blip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8142, "dossier_id": "salesforce/blip/blip-2", "name": "BLIP-2", "maker": "salesforce", "family": "salesforce/blip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot image captioning and visual question answering without training the vision encoder or the LLM end to end.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8143, "dossier_id": "salesforce/blip/blip-diffusion", "name": "BLIP-Diffusion", "maker": "salesforce", "family": "salesforce/blip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot subject-driven generation and faster subject customization, and subject-driven editing when combined with ControlNet.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8144, "dossier_id": "salesforce/blip/blip3o", "name": "BLIP3-o", "maker": "salesforce", "family": "salesforce/blip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8145, "dossier_id": "salesforce/blip/blip3o-next", "name": "BLIP3o-NEXT", "maker": "salesforce", "family": "salesforce/blip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8146, "dossier_id": "salesforce/blip/instructblip", "name": "InstructBLIP", "maker": "salesforce", "family": "salesforce/blip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General vision-language instructions across the datasets used in the paper, with an instruction-aware Q-Former.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8147, "dossier_id": "salesforce/coda/coda", "name": "CoDA", "maker": "salesforce", "family": "salesforce/coda", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8148, "dossier_id": "salesforce/codegen/codegen", "name": "CodeGen", "maker": "salesforce", "family": "salesforce/codegen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8149, "dossier_id": "salesforce/codegen/codegen2", "name": "CodeGen2", "maker": "salesforce", "family": "salesforce/codegen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation, with the paper studying training on programming and natural language. These four checkpoints are the public CodeGen2 weights.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8150, "dossier_id": "salesforce/codegen/codegen2-5", "name": "CodeGen2.5", "maker": "salesforce", "family": "salesforce/codegen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8151, "dossier_id": "salesforce/codet5/codet5", "name": "CodeT5", "maker": "salesforce", "family": "salesforce/codet5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8152, "dossier_id": "salesforce/codet5/codet5p", "name": "CodeT5+", "maker": "salesforce", "family": "salesforce/codet5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code understanding and generation, including embeddings from the 110M checkpoint and instruction-following code generation from InstructCodeT5+.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8153, "dossier_id": "salesforce/ctrl/ctrl", "name": "CTRL", "maker": "salesforce", "family": "salesforce/ctrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8154, "dossier_id": "salesforce/fare/fare", "name": "FARE", "maker": "salesforce", "family": "salesforce/fare", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8155, "dossier_id": "salesforce/fofpred/fofpred", "name": "FOFPred", "maker": "salesforce", "family": "salesforce/fofpred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predict pixel motion for robot control and video generation. The README shows a Diffusers pipeline with trust_remote_code.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8156, "dossier_id": "salesforce/gta1/gta1", "name": "GTA1", "maker": "salesforce", "family": "salesforce/gta1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Ground GUI actions and drive computer-use tasks. The paper is arXiv:2507.05791.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8157, "dossier_id": "salesforce/moirai/moirai-1-0", "name": "Moirai 1.0", "maker": "salesforce", "family": "salesforce/moirai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot forecasting across domains, frequencies, and numbers of variables, including covariates.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8158, "dossier_id": "salesforce/moirai/moirai-1-1", "name": "Moirai 1.1", "maker": "salesforce", "family": "salesforce/moirai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8159, "dossier_id": "salesforce/moirai/moirai-2", "name": "Moirai 2.0", "maker": "salesforce", "family": "salesforce/moirai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8160, "dossier_id": "salesforce/moirai/moirai-agent", "name": "Moirai-Agent", "maker": "salesforce", "family": "salesforce/moirai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8161, "dossier_id": "salesforce/moirai/moirai-moe", "name": "Moirai-MoE", "maker": "salesforce", "family": "salesforce/moirai", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8162, "dossier_id": "salesforce/progen/progen", "name": "ProGen", "maker": "salesforce", "family": "salesforce/progen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8163, "dossier_id": "salesforce/sfr/sfr-embedding-2", "name": "SFR-Embedding-2", "maker": "salesforce", "family": "salesforce/sfr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General text embeddings, as the next SFR-Embedding release after SFR-Embedding-Mistral.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8164, "dossier_id": "salesforce/sfr/sfr-embedding-code", "name": "SFR-Embedding-Code", "maker": "salesforce", "family": "salesforce/sfr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code retrieval. Separate from the English SFR-Embedding text models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8165, "dossier_id": "salesforce/unicontrol/unicontrol", "name": "UniControl", "maker": "salesforce", "family": "salesforce/unicontrol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8166, "dossier_id": "salesforce/xgen/xgen-mm", "name": "xGen-MM (BLIP-3)", "maker": "salesforce", "family": "salesforce/xgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8167, "dossier_id": "salesforce/xgen/xgen-mm-vid", "name": "xGen-MM-Vid (BLIP-3-Video)", "maker": "salesforce", "family": "salesforce/xgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8168, "dossier_id": "salesforce/xgen/xgen-sales", "name": "xGen-Sales", "maker": "salesforce", "family": "salesforce/xgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8169, "dossier_id": "salesforce/xlam/xlam", "name": "xLAM", "maker": "salesforce", "family": "salesforce/xlam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8170, "dossier_id": "salesforce/xlam/xlam-2", "name": "xLAM-2", "maker": "salesforce", "family": "salesforce/xlam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tool use and function calling for agents, including multi-turn data from the APIGen-MT set tagged on the Hub.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8171, "dossier_id": "sambanova/qwq/qwq-0-5b-sft-draft", "name": "QwQ-0.5B-SFT-Draft", "maker": "sambanova", "family": "sambanova/qwq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8172, "dossier_id": "sambanova/samba/samba-1", "name": "Samba-1", "maker": "sambanova", "family": "sambanova/samba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8173, "dossier_id": "sambanova/samba/samba-coe", "name": "Samba-CoE", "maker": "sambanova", "family": "sambanova/samba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8174, "dossier_id": "sambanova/toolbench/codegen-16b-mono-toolbench", "name": "CodeGen-16B-mono ToolBench", "maker": "sambanova", "family": "sambanova/toolbench", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "API-based action generation for the ToolBench evaluation. The authors say the checkpoints are only for the initial study.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8175, "dossier_id": "sambanova/toolbench/llama-30b-toolbench", "name": "LLaMA-30B ToolBench", "maker": "sambanova", "family": "sambanova/toolbench", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "API-based action generation for the ToolBench evaluation. The authors say the checkpoints are only for the initial study.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8176, "dossier_id": "sambanova/toolbench/starcoder-toolbench", "name": "StarCoder ToolBench", "maker": "sambanova", "family": "sambanova/toolbench", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "API-based action generation for the ToolBench evaluation. The authors say the checkpoints are only for the initial study.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8177, "dossier_id": "samsung/gauss/gauss-2-3", "name": "Gauss 2.3", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "rumor", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8178, "dossier_id": "samsung/gauss/gauss-2-3-think", "name": "Gauss 2.3 Think", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "rumor", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8179, "dossier_id": "samsung/gauss/gauss-code", "name": "Samsung Gauss Code", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8180, "dossier_id": "samsung/gauss/gauss-image", "name": "Samsung Gauss Image", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Samsung says it generates and edits images, changes style, adds content, and upscales low-resolution images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8181, "dossier_id": "samsung/gauss/gauss-language", "name": "Samsung Gauss Language", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Samsung says it drafts email, summarizes documents, translates content, and can support smarter device control if built into products.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8182, "dossier_id": "samsung/gauss/gauss-o-flash", "name": "Gauss O Flash", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "rumor", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8183, "dossier_id": "samsung/gauss/gauss-o4", "name": "Gauss O4", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "rumor", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8184, "dossier_id": "samsung/gauss/gauss2-balanced", "name": "Samsung Gauss2 Balanced", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8185, "dossier_id": "samsung/gauss/gauss2-compact", "name": "Samsung Gauss2 Compact", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8186, "dossier_id": "samsung/gauss/gauss2-supreme", "name": "Samsung Gauss2 Supreme", "maker": "samsung", "family": "samsung/gauss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8187, "dossier_id": "samsung/health/himae", "name": "HiMAE", "maker": "samsung", "family": "samsung/health", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8188, "dossier_id": "samsung/health/xmae", "name": "xMAE", "maker": "samsung", "family": "samsung/health", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8189, "dossier_id": "samsung/samsone/samsone-134m", "name": "Samsone-134M", "maker": "samsung", "family": "samsung/samsone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8190, "dossier_id": "samsung/samsone/samsone-356m", "name": "Samsone-356M", "maker": "samsung", "family": "samsung/samsone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8191, "dossier_id": "samsung/samsone/samsone-99m", "name": "Samsone-99M", "maker": "samsung", "family": "samsung/samsone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8192, "dossier_id": "sapienza/camoscio/camoscio-7b", "name": "Camoscio 7B", "maker": "sapienza", "family": "sapienza/camoscio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8193, "dossier_id": "sapienza/dantellm/dantellm-7b-instruct-italian-v0-1", "name": "DanteLLM 7B Instruct Italian v0.1", "maker": "sapienza", "family": "sapienza/dantellm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8194, "dossier_id": "sapienza/fauno/fauno-13b", "name": "Fauno 13B", "maker": "sapienza", "family": "sapienza/fauno", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8195, "dossier_id": "sapienza/fauno/fauno-7b", "name": "Fauno 7B", "maker": "sapienza", "family": "sapienza/fauno", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8196, "dossier_id": "sapienza/maverick/maverick-mes-litbank", "name": "Maverick-mes LitBank", "maker": "sapienza", "family": "sapienza/maverick", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8197, "dossier_id": "sapienza/maverick/maverick-mes-ontonotes", "name": "Maverick-mes OntoNotes", "maker": "sapienza", "family": "sapienza/maverick", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8198, "dossier_id": "sapienza/maverick/maverick-mes-preco", "name": "Maverick-mes PreCo", "maker": "sapienza", "family": "sapienza/maverick", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8199, "dossier_id": "sapienza/relik/relik-entity-linking-base", "name": "ReLiK entity linking base", "maker": "sapienza", "family": "sapienza/relik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8200, "dossier_id": "sapienza/relik/relik-entity-linking-large", "name": "ReLiK entity linking large", "maker": "sapienza", "family": "sapienza/relik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8201, "dossier_id": "sapienza/relik/relik-entity-linking-small", "name": "ReLiK entity linking small", "maker": "sapienza", "family": "sapienza/relik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8202, "dossier_id": "sapienza/relik/relik-relation-extraction-nyt-large", "name": "ReLiK relation extraction NYT large", "maker": "sapienza", "family": "sapienza/relik", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8203, "dossier_id": "saskatchewan/deep-plant-phenomics/bbox-regressor-lemnatec", "name": "Lemnatec Bounding Box Regressor", "maker": "saskatchewan", "family": "saskatchewan/deep-plant-phenomics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8204, "dossier_id": "saskatchewan/deep-plant-phenomics/canola-flower-counter", "name": "Canola Flower Counter", "maker": "saskatchewan", "family": "saskatchewan/deep-plant-phenomics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8205, "dossier_id": "saskatchewan/deep-plant-phenomics/rosette-leaf-regressor", "name": "Rosette Leaf Regressor", "maker": "saskatchewan", "family": "saskatchewan/deep-plant-phenomics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8206, "dossier_id": "saskatchewan/deep-plant-phenomics/vegetation-segmentation-network", "name": "Vegetation Segmentation Network", "maker": "saskatchewan", "family": "saskatchewan/deep-plant-phenomics", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8207, "dossier_id": "saskatchewan/deepwheat/deepwheat", "name": "DeepWheat", "maker": "saskatchewan", "family": "saskatchewan/deepwheat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8208, "dossier_id": "sber/fred-t5/fred-t5-1-7b", "name": "FRED-T5 1.7B", "maker": "sber", "family": "sber/fred-t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8209, "dossier_id": "sber/fred-t5/fred-t5-large", "name": "FRED-T5 large", "maker": "sber", "family": "sber/fred-t5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8210, "dossier_id": "sber/frida/frida-decisions", "name": "FRIDA-Decisions", "maker": "sber", "family": "sber/frida", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8211, "dossier_id": "sber/gfusion/gfusion-10b-a1-8b", "name": "GFusion-10B-A1.8B", "maker": "sber", "family": "sber/gfusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8212, "dossier_id": "sber/giga-embeddings/giga-embeddings-instruct-10b", "name": "Giga-Embeddings-instruct 10B-A1.8B", "maker": "sber", "family": "sber/giga-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8213, "dossier_id": "sber/giga-embeddings/giga-embeddings-instruct-3b-0826", "name": "Giga-Embeddings-instruct 3B (0826)", "maker": "sber", "family": "sber/giga-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8214, "dossier_id": "sber/giga-embeddings/giga-embeddings-instruct-480m", "name": "Giga-Embeddings-instruct 480M", "maker": "sber", "family": "sber/giga-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8215, "dossier_id": "sber/giga-embeddings/giga-retrieval-instruct", "name": "Giga-Retrieval-instruct", "maker": "sber", "family": "sber/giga-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8216, "dossier_id": "sber/gigaam/gigaam-multilingual", "name": "GigaAM Multilingual", "maker": "sber", "family": "sber/gigaam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8217, "dossier_id": "sber/gigaam/gigaam-v1", "name": "GigaAM v1", "maker": "sber", "family": "sber/gigaam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8218, "dossier_id": "sber/gigaam/gigaam-v2", "name": "GigaAM v2", "maker": "sber", "family": "sber/gigaam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8219, "dossier_id": "sber/gigaam/gigaam-v3", "name": "GigaAM v3", "maker": "sber", "family": "sber/gigaam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8220, "dossier_id": "sber/gigachat/gigachat", "name": "GigaChat", "maker": "sber", "family": "sber/gigachat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8221, "dossier_id": "sber/gigachat/gigachat-20b-a3b-base", "name": "GigaChat-20B-A3B-base", "maker": "sber", "family": "sber/gigachat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8222, "dossier_id": "sber/gigachat/gigachat-3-1-audio", "name": "GigaChat 3.1 Audio", "maker": "sber", "family": "sber/gigachat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8223, "dossier_id": "sber/gigachat/gigachat-3-1-lightning", "name": "GigaChat 3.1 Lightning", "maker": "sber", "family": "sber/gigachat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8224, "dossier_id": "sber/gigachat/gigachat-3-5-reasoning", "name": "GigaChat 3.5 Ultra Reasoning", "maker": "sber", "family": "sber/gigachat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8225, "dossier_id": "sber/gigachat/gigachat-3-5-ultra", "name": "GigaChat 3.5 Ultra", "maker": "sber", "family": "sber/gigachat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8226, "dossier_id": "sber/gigachat/gigachat-max", "name": "GigaChat Max", "maker": "sber", "family": "sber/gigachat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8227, "dossier_id": "sber/gigachat/gigachat-pro", "name": "GigaChat Pro", "maker": "sber", "family": "sber/gigachat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8228, "dossier_id": "sber/kandinsky/kandinsky-1", "name": "Kandinsky", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8229, "dossier_id": "sber/kandinsky/kandinsky-2-0", "name": "Kandinsky 2.0", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8230, "dossier_id": "sber/kandinsky/kandinsky-2-1", "name": "Kandinsky 2.1", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8231, "dossier_id": "sber/kandinsky/kandinsky-2-2", "name": "Kandinsky 2.2", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8232, "dossier_id": "sber/kandinsky/kandinsky-3-0", "name": "Kandinsky 3.0", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8233, "dossier_id": "sber/kandinsky/kandinsky-3-1", "name": "Kandinsky 3.1", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8234, "dossier_id": "sber/kandinsky/kandinsky-3-controlnet", "name": "Kandinsky 3 ControlNet", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8235, "dossier_id": "sber/kandinsky/kandinsky-3-flash", "name": "Kandinsky Flash", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8236, "dossier_id": "sber/kandinsky/kandinsky-3-ip-adapter", "name": "Kandinsky 3 IP-Adapter", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Sber's Kandinsky 3 IP-Adapter is an open image-prompt adapter. Collected 2026-10-15. The card body was not read.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8237, "dossier_id": "sber/kandinsky/kandinsky-4-t2v-flash", "name": "Kandinsky 4.0 T2V Flash", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8238, "dossier_id": "sber/kandinsky/kandinsky-4-v2a", "name": "Kandinsky 4.0 V2A", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8239, "dossier_id": "sber/kandinsky/kandinsky-5-i2i-lite", "name": "Kandinsky 5.0 I2I Lite", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Sber Kandinsky Lab's Kandinsky 5.0 image editor is an open 6B image-to-image model. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8240, "dossier_id": "sber/kandinsky/kandinsky-5-i2v-lite", "name": "Kandinsky 5.0 I2V Lite", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8241, "dossier_id": "sber/kandinsky/kandinsky-5-i2v-pro", "name": "Kandinsky 5.0 I2V Pro", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8242, "dossier_id": "sber/kandinsky/kandinsky-5-t2i-lite", "name": "Kandinsky 5.0 T2I Lite", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8243, "dossier_id": "sber/kandinsky/kandinsky-5-t2v-lite", "name": "Kandinsky 5.0 T2V Lite", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8244, "dossier_id": "sber/kandinsky/kandinsky-5-t2v-pro", "name": "Kandinsky 5.0 T2V Pro", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8245, "dossier_id": "sber/kandinsky/kandinsky-6-lite", "name": "Kandinsky 6.0 Video Lite", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8246, "dossier_id": "sber/kandinsky/kandinsky-6-pro", "name": "Kandinsky 6.0 Video Pro", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8247, "dossier_id": "sber/kandinsky/kandinsky-6-vsr", "name": "Kandinsky 6.0 Video Super-Resolution", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8248, "dossier_id": "sber/kandinsky/kandinsky-video-1-0", "name": "Kandinsky Video 1.0", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8249, "dossier_id": "sber/kandinsky/kandinsky-video-1-1", "name": "Kandinsky Video 1.1", "maker": "sber", "family": "sber/kandinsky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8250, "dossier_id": "sber/mgpt/mgpt", "name": "mGPT", "maker": "sber", "family": "sber/mgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8251, "dossier_id": "sber/mgpt/mgpt-13b", "name": "mGPT 13B", "maker": "sber", "family": "sber/mgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8252, "dossier_id": "sber/movqgan/movqgan", "name": "MoVQGAN", "maker": "sber", "family": "sber/movqgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8253, "dossier_id": "sber/pollux/pollux-4b-judge", "name": "Pollux 4B Judge", "maker": "sber", "family": "sber/pollux", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8254, "dossier_id": "sber/ru-encoder/ru-en-rosberta", "name": "ru-en-RoSBERTa", "maker": "sber", "family": "sber/ru-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8255, "dossier_id": "sber/ru-encoder/rubert-base", "name": "ruBERT base", "maker": "sber", "family": "sber/ru-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8256, "dossier_id": "sber/ru-encoder/rubert-large", "name": "ruBERT large", "maker": "sber", "family": "sber/ru-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8257, "dossier_id": "sber/ru-encoder/ruroberta-large", "name": "ruRoBERTa large", "maker": "sber", "family": "sber/ru-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8258, "dossier_id": "sber/ru-encoder/sbert-large-nlu-ru", "name": "SBERT large NLU Russian", "maker": "sber", "family": "sber/ru-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8259, "dossier_id": "sber/rudalle/rudalle-emojich", "name": "ruDALL-E Emojich", "maker": "sber", "family": "sber/rudalle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8260, "dossier_id": "sber/rudalle/rudalle-malevich", "name": "ruDALL-E Malevich", "maker": "sber", "family": "sber/rudalle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8261, "dossier_id": "sber/rudolph/rudolph-1-3b", "name": "RUDOLPH 1.3B", "maker": "sber", "family": "sber/rudolph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8262, "dossier_id": "sber/rudolph/rudolph-2-7b", "name": "RUDOLPH 2.7B", "maker": "sber", "family": "sber/rudolph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8263, "dossier_id": "sber/rudolph/rudolph-350m", "name": "RUDOLPH 350M", "maker": "sber", "family": "sber/rudolph", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8264, "dossier_id": "sber/rugpt/rugpt-3-5-13b", "name": "ruGPT-3.5 13B", "maker": "sber", "family": "sber/rugpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8265, "dossier_id": "sber/rugpt/rugpt-3-large", "name": "ruGPT-3 Large", "maker": "sber", "family": "sber/rugpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8266, "dossier_id": "sber/rugpt/rugpt-3-medium", "name": "ruGPT-3 Medium", "maker": "sber", "family": "sber/rugpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8267, "dossier_id": "sber/rugpt/rugpt-3-small", "name": "ruGPT-3 Small", "maker": "sber", "family": "sber/rugpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8268, "dossier_id": "sber/rugpt/rugpt-3-xl", "name": "ruGPT-3 XL", "maker": "sber", "family": "sber/rugpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8269, "dossier_id": "sber/zarya/zarya-0-6b", "name": "Zarya 0.6B", "maker": "sber", "family": "sber/zarya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8270, "dossier_id": "sber/zarya/zarya-1-7b", "name": "Zarya 1.7B", "maker": "sber", "family": "sber/zarya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8271, "dossier_id": "sber/zarya/zarya-4b", "name": "Zarya 4B", "maker": "sber", "family": "sber/zarya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8272, "dossier_id": "sber/zarya/zarya-orthrus-0-6b", "name": "Zarya-Orthrus 0.6B", "maker": "sber", "family": "sber/zarya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sber's Zarya-Orthrus 0.6B is an open MIT hybrid checkpoint uploaded 22 September 2026. Collected 2026-10-15.", "summary_intro": "Sber's Zarya-Orthrus 0.6B is an open MIT hybrid checkpoint uploaded 22 September 2026. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8273, "dossier_id": "sber/zarya/zarya-orthrus-1-7b", "name": "Zarya-Orthrus 1.7B", "maker": "sber", "family": "sber/zarya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sber's Zarya-Orthrus 1.7B is an open MIT hybrid checkpoint uploaded 22 September 2026. Collected 2026-10-15.", "summary_intro": "Sber's Zarya-Orthrus 1.7B is an open MIT hybrid checkpoint uploaded 22 September 2026. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8274, "dossier_id": "scarolina/blmm/crystal-transformer", "name": "Crystal Transformer", "maker": "scarolina", "family": "scarolina/blmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8275, "dossier_id": "scarolina/mtransformer/mt-bart", "name": "MT-BART", "maker": "scarolina", "family": "scarolina/mtransformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8276, "dossier_id": "scarolina/mtransformer/mt-gpt", "name": "MT-GPT", "maker": "scarolina", "family": "scarolina/mtransformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8277, "dossier_id": "scarolina/mtransformer/mt-gpt2", "name": "MT-GPT2", "maker": "scarolina", "family": "scarolina/mtransformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8278, "dossier_id": "scarolina/mtransformer/mt-gptj", "name": "MT-GPTJ", "maker": "scarolina", "family": "scarolina/mtransformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8279, "dossier_id": "scarolina/mtransformer/mt-gptneo", "name": "MT-GPTNeo", "maker": "scarolina", "family": "scarolina/mtransformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8280, "dossier_id": "scarolina/mtransformer/mt-roberta", "name": "MT-RoBERTa", "maker": "scarolina", "family": "scarolina/mtransformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8281, "dossier_id": "scarolina/uflowcsp/uflowcsp", "name": "uFlowCSP", "maker": "scarolina", "family": "scarolina/uflowcsp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8282, "dossier_id": "scenario/flux-1/3d-blocky-elements", "name": "3D Blocky Elements", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8283, "dossier_id": "scenario/flux-1/3d-chibis", "name": "3D Chibis", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8284, "dossier_id": "scenario/flux-1/3d-fantasy-chibis", "name": "3D Fantasy Chibis", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8285, "dossier_id": "scenario/flux-1/3d-icons", "name": "3D Icons", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8286, "dossier_id": "scenario/flux-1/80s-cyberpunk-by-fofr", "name": "80s Cyberpunk (by Fofr)", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "This Flux.1 LoRA renders 1980s retro-futuristic cyberpunk visuals: neon-lit cityscapes, dystopian streets, and cinematic glow aesthetics.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8287, "dossier_id": "scenario/flux-1/action-plastic-2-0", "name": "Action Plastic 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8288, "dossier_id": "scenario/flux-1/ai-engravings", "name": "AI Engravings", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8289, "dossier_id": "scenario/flux-1/alley-brawler", "name": "Alley Brawler", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8290, "dossier_id": "scenario/flux-1/animated-feature-2-0", "name": "Animated Feature 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8291, "dossier_id": "scenario/flux-1/anime-fantasy-characters-2-0", "name": "Anime Fantasy Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8292, "dossier_id": "scenario/flux-1/anime-watercolor-character-2-0", "name": "Anime Watercolor character 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8293, "dossier_id": "scenario/flux-1/arcade-hero-2-0", "name": "Arcade Hero 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8294, "dossier_id": "scenario/flux-1/battle-arenas-2-0", "name": "Battle Arenas 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8295, "dossier_id": "scenario/flux-1/black-and-white-patterns", "name": "Black-and-White Patterns", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8296, "dossier_id": "scenario/flux-1/bold-comic-scenes", "name": "Bold Comic Scenes", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8297, "dossier_id": "scenario/flux-1/bold-line-environments-2-0", "name": "Bold Line Environments 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8298, "dossier_id": "scenario/flux-1/bold-lines-illustrations-2-0", "name": "Bold Lines Illustrations 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8299, "dossier_id": "scenario/flux-1/boxes-crates-2-0", "name": "Boxes & Crates 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8300, "dossier_id": "scenario/flux-1/card-frames", "name": "Card Frames", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8301, "dossier_id": "scenario/flux-1/cartoon-backgrounds-2-0", "name": "Cartoon Backgrounds 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8302, "dossier_id": "scenario/flux-1/cartoon-characters-1-0", "name": "Cartoon Characters 1.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8303, "dossier_id": "scenario/flux-1/cartoon-characters-2-0", "name": "Cartoon Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux.1 LoRA for stylized 3D cartoon characters with vibrant colors and exaggerated proportions, built for games and animation assets.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8304, "dossier_id": "scenario/flux-1/cartoon-costume-sets", "name": "Cartoon Costume Sets", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8305, "dossier_id": "scenario/flux-1/cartoons-objects-2-0", "name": "Cartoons Objects 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8306, "dossier_id": "scenario/flux-1/casual-environments-2-0", "name": "Casual Environments 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A Flux.1 LoRA for stylized 2D environments with a warm, bright palette. Generates whimsical landscapes, interiors, and outdoor scenes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8307, "dossier_id": "scenario/flux-1/chinese-fairytale", "name": "Chinese Fairytale", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A Flux.1 LoRA that generates traditional Chinese fairytale art: whimsical characters, mythical creatures, and vivid watercolor illustration style.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8308, "dossier_id": "scenario/flux-1/cinematic-realism", "name": "Cinematic Realism", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8309, "dossier_id": "scenario/flux-1/classic-french-roast-2-0", "name": "Classic French Roast 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8310, "dossier_id": "scenario/flux-1/colorful-digital-icons", "name": "Colorful Digital Icons", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8311, "dossier_id": "scenario/flux-1/coloring-book", "name": "Coloring Book", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8312, "dossier_id": "scenario/flux-1/comic-adventures", "name": "Comic Adventures", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8313, "dossier_id": "scenario/flux-1/creepy-style-character-icons-2-0", "name": "Creepy Style Character Icons 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8314, "dossier_id": "scenario/flux-1/cybernetic-textures-2-0", "name": "Cybernetic Textures 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8315, "dossier_id": "scenario/flux-1/dark-anime-2-0", "name": "Dark Anime 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8316, "dossier_id": "scenario/flux-1/dark-inked-characters-2-0", "name": "Dark Inked Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8317, "dossier_id": "scenario/flux-1/decker", "name": "Decker", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8318, "dossier_id": "scenario/flux-1/digitally-painted-creatures-2-0", "name": "Digitally Painted Creatures 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8319, "dossier_id": "scenario/flux-1/dusty-paperback", "name": "Dusty Paperback", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8320, "dossier_id": "scenario/flux-1/edgecraft-enhanced-stylistic-rendering", "name": "EdgeCraft - Enhanced Stylistic Rendering", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8321, "dossier_id": "scenario/flux-1/edgy-comic", "name": "Edgy Comic", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8322, "dossier_id": "scenario/flux-1/el-diablo", "name": "El Diablo", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8323, "dossier_id": "scenario/flux-1/environment-sprites-2-0", "name": "Environment Sprites 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8324, "dossier_id": "scenario/flux-1/epic-ai-paintings", "name": "Epic AI Paintings", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8325, "dossier_id": "scenario/flux-1/ethereal-beings", "name": "Ethereal Beings", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8326, "dossier_id": "scenario/flux-1/euro-comix", "name": "Euro Comix", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8327, "dossier_id": "scenario/flux-1/expressive-comic-characters", "name": "Expressive Comic Characters", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8328, "dossier_id": "scenario/flux-1/fantasy-blades", "name": "Fantasy Blades", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8329, "dossier_id": "scenario/flux-1/flux-retro-aesthetics", "name": "Flux Retro Aesthetics", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8330, "dossier_id": "scenario/flux-1/future-war", "name": "Future War", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8331, "dossier_id": "scenario/flux-1/game-backgrounds-iii", "name": "Game Backgrounds - III", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8332, "dossier_id": "scenario/flux-1/game-backgrounds-iv", "name": "Game Backgrounds - IV", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8333, "dossier_id": "scenario/flux-1/game-ui-essentials-2-0", "name": "Game UI Essentials 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8334, "dossier_id": "scenario/flux-1/greater-tiddlewick", "name": "Greater Tiddlewick", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8335, "dossier_id": "scenario/flux-1/gritty-comics-2-0", "name": "Gritty Comics 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8336, "dossier_id": "scenario/flux-1/gwendolyn-2-0", "name": "Gwendolyn 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8337, "dossier_id": "scenario/flux-1/hand-drawn-fantasy", "name": "Hand-drawn Fantasy", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8338, "dossier_id": "scenario/flux-1/hand-painted-textures", "name": "Hand-Painted Textures", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8339, "dossier_id": "scenario/flux-1/hand-painted-textures-2-0", "name": "Hand Painted Textures 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux LoRA for seamless, hand-painted textures. Generates stylized wood, stone, and organic patterns ideal for game assets and animations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8340, "dossier_id": "scenario/flux-1/heartcore", "name": "Heartcore", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8341, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8342, "dossier_id": "scenario/flux-1/human-interface-2-0", "name": "Human Interface 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8343, "dossier_id": "scenario/flux-1/hyper-casual-characters-2-0", "name": "Hyper Casual Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8344, "dossier_id": "scenario/flux-1/hyper-realistic-flux", "name": "Hyper Realistic Flux", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8345, "dossier_id": "scenario/flux-1/i-like-big-bots", "name": "I Like Big Bots", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8346, "dossier_id": "scenario/flux-1/imaginative-isometrics-2-0", "name": "Imaginative Isometrics 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8347, "dossier_id": "scenario/flux-1/isometric-background", "name": "Isometric Background", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux LoRA for storybook-style isometric backgrounds. Generates warm, detailed fantasy scenes: villages, forests, medieval towns, and architectural environments.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8348, "dossier_id": "scenario/flux-1/juicy-by-deacon", "name": "Juicy by Deacon", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8349, "dossier_id": "scenario/flux-1/juicy-icons-2-0", "name": "Juicy Icons 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8350, "dossier_id": "scenario/flux-1/juno", "name": "Juno", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8351, "dossier_id": "scenario/flux-1/kawaii", "name": "Kawaii", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8352, "dossier_id": "scenario/flux-1/legendary-tcg-characters-2-0", "name": "Legendary TCG Characters 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8353, "dossier_id": "scenario/flux-1/lineart-textures", "name": "Lineart Textures", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8354, "dossier_id": "scenario/flux-1/luminous-depths", "name": "Luminous Depths", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8355, "dossier_id": "scenario/flux-1/midsummer-blues", "name": "Midsummer Blues", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8356, "dossier_id": "scenario/flux-1/minimalism", "name": "Minimalism", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8357, "dossier_id": "scenario/flux-1/minimalist-3d-food-icons", "name": "Minimalist 3D Food Icons", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8358, "dossier_id": "scenario/flux-1/minimalist-3d-props-2-0", "name": "Minimalist 3D Props 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8359, "dossier_id": "scenario/flux-1/minimalist-fantasy-chibis", "name": "Minimalist Fantasy Chibis", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8360, "dossier_id": "scenario/flux-1/minimalist-icons", "name": "Minimalist Icons", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8361, "dossier_id": "scenario/flux-1/modern-animated-fantasy-creatures-2-0", "name": "Modern Animated Fantasy Creatures 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8362, "dossier_id": "scenario/flux-1/natures-bounty", "name": "Natures Bounty", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8363, "dossier_id": "scenario/flux-1/neo3d-realism", "name": "Neo3D Realism", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8364, "dossier_id": "scenario/flux-1/northern-graphite", "name": "Northern Graphite", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8365, "dossier_id": "scenario/flux-1/old-pastel", "name": "Old Pastel", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8366, "dossier_id": "scenario/flux-1/overworld-maps", "name": "Overworld Maps", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8367, "dossier_id": "scenario/flux-1/painterly-scenes", "name": "Painterly Scenes", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8368, "dossier_id": "scenario/flux-1/plaster-concrete-textures", "name": "Plaster & Concrete Textures", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8369, "dossier_id": "scenario/flux-1/plaster-walls-textures", "name": "Plaster Walls Textures", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8370, "dossier_id": "scenario/flux-1/plastic-fantastic", "name": "Plastic Fantastic", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8371, "dossier_id": "scenario/flux-1/platformer-props", "name": "Platformer Props", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8372, "dossier_id": "scenario/flux-1/poster-gouache", "name": "Poster Gouache", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8373, "dossier_id": "scenario/flux-1/puffy-icons-3-0", "name": "Puffy Icons 3.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8374, "dossier_id": "scenario/flux-1/rad-racers-2-0", "name": "Rad Racers 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8375, "dossier_id": "scenario/flux-1/realistic-textures-2-0", "name": "Realistic Textures 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8376, "dossier_id": "scenario/flux-1/realistic-textures-3-0", "name": "Realistic Textures 3.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8377, "dossier_id": "scenario/flux-1/red-haired-superheroine", "name": "Red-haired Superheroine", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8378, "dossier_id": "scenario/flux-1/rochelle-2-0", "name": "Rochelle 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8379, "dossier_id": "scenario/flux-1/rpg-avatars", "name": "RPG Avatars", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8380, "dossier_id": "scenario/flux-1/rpg-character-trait-avatars-2-0", "name": "RPG Character Trait Avatars 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8381, "dossier_id": "scenario/flux-1/sacred-geometry", "name": "Sacred Geometry", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8382, "dossier_id": "scenario/flux-1/saturday-morning", "name": "Saturday Morning", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8383, "dossier_id": "scenario/flux-1/simple-vector-v2", "name": "Simple Vector v2", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8384, "dossier_id": "scenario/flux-1/sketch-paint-flux", "name": "Sketch Paint Flux", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8385, "dossier_id": "scenario/flux-1/sleek-3d-assets-2-0", "name": "Sleek 3D Assets 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8386, "dossier_id": "scenario/flux-1/small-warriors", "name": "Small Warriors", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8387, "dossier_id": "scenario/flux-1/stage-dressing", "name": "Stage Dressing", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8388, "dossier_id": "scenario/flux-1/sticker-icons-2-0", "name": "Sticker Icons 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8389, "dossier_id": "scenario/flux-1/street-punks", "name": "Street Punks", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8390, "dossier_id": "scenario/flux-1/striking-visage", "name": "Striking Visage", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux.1 LoRA for stylized 3D fantasy characters. Generates armored warriors with sculpted detail, expressive faces, and dragon companions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8391, "dossier_id": "scenario/flux-1/stylized-3d", "name": "Stylized 3D", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8392, "dossier_id": "scenario/flux-1/stylized-game-icons-props", "name": "Stylized Game Icons & Props", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8393, "dossier_id": "scenario/flux-1/super-top-down-2-0", "name": "Super Top-Down 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8394, "dossier_id": "scenario/flux-1/swiss-cobblestone-textures", "name": "Swiss Cobblestone Textures", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8395, "dossier_id": "scenario/flux-1/textures-brick-walls", "name": "Textures - Brick Walls", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux LoRA for generating realistic brick wall textures, from classic red and whitewashed styles to weathered, aged, and mossy surfaces.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8396, "dossier_id": "scenario/flux-1/textures-concrete-surfaces", "name": "Textures - Concrete Surfaces", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8397, "dossier_id": "scenario/flux-1/textures-floors", "name": "Textures - Floors", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "This Flux.1 LoRA generates seamless flooring textures: tiles, stone, cobblestone, wood planks, herringbone patterns, and natural ground surfaces.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8398, "dossier_id": "scenario/flux-1/textures-marble-surfaces", "name": "Textures - Marble Surfaces", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8399, "dossier_id": "scenario/flux-1/textures-stone-walls", "name": "Textures - Stone Walls", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8400, "dossier_id": "scenario/flux-1/textures-terracotta-roofs", "name": "Textures - Terracotta Roofs", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8401, "dossier_id": "scenario/flux-1/textures-wood-boards", "name": "Textures - Wood Boards", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8402, "dossier_id": "scenario/flux-1/the-green-skinned-zombie", "name": "The Green-skinned Zombie", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8403, "dossier_id": "scenario/flux-1/the-hidden-vale", "name": "The Hidden Vale", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8404, "dossier_id": "scenario/flux-1/the-housing-market", "name": "The Housing Market", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8405, "dossier_id": "scenario/flux-1/the-jolly-guy", "name": "The Jolly Guy", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8406, "dossier_id": "scenario/flux-1/the-revengers", "name": "The Revengers", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8407, "dossier_id": "scenario/flux-1/the-sorceress", "name": "The Sorceress", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8408, "dossier_id": "scenario/flux-1/the-tiefling-woman", "name": "The Tiefling Woman", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8409, "dossier_id": "scenario/flux-1/the-usual-suspects", "name": "The Usual Suspects", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8410, "dossier_id": "scenario/flux-1/top-down-td-game", "name": "Top-down TD Game", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8411, "dossier_id": "scenario/flux-1/toy-box-by-renderartist", "name": "Toy Box (by RenderArtist)", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8412, "dossier_id": "scenario/flux-1/ui-frame-generator-2-0", "name": "UI Frame Generator 2.0", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8413, "dossier_id": "scenario/flux-1/vfx-hits", "name": "VFX Hits", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8414, "dossier_id": "scenario/flux-1/vibrant-comic", "name": "Vibrant Comic", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8415, "dossier_id": "scenario/flux-1/vibrant-heroics", "name": "Vibrant Heroics", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8416, "dossier_id": "scenario/flux-1/vibrant-storybook-illustrations", "name": "Vibrant Storybook Illustrations", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8417, "dossier_id": "scenario/flux-1/vibrant-surreal-landscapes", "name": "Vibrant Surreal Landscapes", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux.1 LoRA for glossy, candy-colored surreal landscapes. Generates dripping liquids, swirling textures, and vibrant floral scenes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8418, "dossier_id": "scenario/flux-1/wood-carving-textures", "name": "Wood Carving Textures", "maker": "scenario", "family": "scenario/flux-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8419, "dossier_id": "scenario/flux-2/3d-platformer-environments-2-0", "name": "3D Platformer Environments 2.0", "maker": "scenario", "family": "scenario/flux-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8420, "dossier_id": "scenario/flux-2/cartoon-costume-sets-2-0", "name": "Cartoon Costume Sets 2.0", "maker": "scenario", "family": "scenario/flux-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8421, "dossier_id": "scenario/flux-2/gem-icons-v2", "name": "Gem Icons v2", "maker": "scenario", "family": "scenario/flux-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux.2 LoRA for stylized game icons: gemstones, crystals, and enchanted loot rendered with vibrant colors and polished digital painting.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8422, "dossier_id": "scenario/flux-composition/2d-vale", "name": "2D Vale", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8423, "dossier_id": "scenario/flux-composition/altered-cabron", "name": "Altered Cabr\u00f3n", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux LoRA that generates characters blending cybernetic and organic forms, producing futuristic hybrid figures with intricate technological and biological detail.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8424, "dossier_id": "scenario/flux-composition/buildings-of-the-vale", "name": "Buildings of The Vale", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8425, "dossier_id": "scenario/flux-composition/childs-play", "name": "Childs Play", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A Flux LoRA that renders children's book illustrations with thick outline art and bold, flat colors across any subject.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8426, "dossier_id": "scenario/flux-composition/cutesy-3d", "name": "Cutesy 3D", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8427, "dossier_id": "scenario/flux-composition/emojis-designer-2-0", "name": "Emojis Designer 2.0", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8428, "dossier_id": "scenario/flux-composition/forever-after", "name": "Forever After", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Forever After generates fairytale storybook illustrations with soft lines and rich, warm colors in a whimsical animation-inspired style.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8429, "dossier_id": "scenario/flux-composition/future-fantastic", "name": "Future Fantastic", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8430, "dossier_id": "scenario/flux-composition/juno-multi-loras", "name": "Juno: Multi-LoRAs", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8431, "dossier_id": "scenario/flux-composition/modern-fantasy", "name": "Modern Fantasy", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A Flux LoRA for colorful, dynamic fantasy character art. Ideal for RPG and TCG illustrations with vibrant modern fantasy aesthetics.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8432, "dossier_id": "scenario/flux-composition/rpg-environment", "name": "RPG Environment", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8433, "dossier_id": "scenario/flux-composition/shades-of-gray", "name": "Shades of Gray", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A Flux multi-LoRA that generates detailed grayscale illustrations with rich textures and dramatic contrasts, evoking pencil and charcoal artistry.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8434, "dossier_id": "scenario/flux-composition/sleek-3d-characters", "name": "Sleek 3D Characters", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux.1 LoRA for polished, stylized 3D character art. Generates warriors, robots, and wizards with expressive faces and detailed outfits.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8435, "dossier_id": "scenario/flux-composition/sleek-3d-isometrics", "name": "Sleek 3D Isometrics", "maker": "scenario", "family": "scenario/flux-composition", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8436, "dossier_id": "scenario/flux-kontext/abandoned-structures-kontext", "name": "Abandoned Structures - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A Flux Kontext LoRA that ages clean building images into worn, crumbling ruins, preserving the original structure, layout, and perspective.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8437, "dossier_id": "scenario/flux-kontext/blockout-to-render-kontext", "name": "Blockout to Render - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flux Kontext LoRA that transforms 3D blockout shapes into photorealistic renders, preserving the original geometry and spatial layout.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8438, "dossier_id": "scenario/flux-kontext/character-turnaround-kontext", "name": "Character Turnaround - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate a four-view character turnaround sheet from any character image, showing every side on a clean background.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8439, "dossier_id": "scenario/flux-kontext/facial-expression-sheet-kontext", "name": "Facial Expression Sheet - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Turns a character image into a 9-emotion expression sheet, preserving their look and art style.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8440, "dossier_id": "scenario/flux-kontext/isometric-tile-maker-kontext", "name": "Isometric Tile Maker - Kontext", "maker": "scenario", "family": "scenario/flux-kontext", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8441, "dossier_id": "scenario/skybox/hdri-skybox", "name": "HDRi Skybox", "maker": "scenario", "family": "scenario/skybox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Convert a standard skybox image into an HDRi environment, saved as EXR and HDR files, with optional cubemap output.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8442, "dossier_id": "scenario/skybox/scenario-skybox-flux-1", "name": "Scenario Skybox Flux.1", "maker": "scenario", "family": "scenario/skybox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8443, "dossier_id": "scenario/skybox/scenario-skybox-gpt", "name": "Scenario Skybox GPT", "maker": "scenario", "family": "scenario/skybox", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8444, "dossier_id": "scenario/texture/scenario-texture", "name": "Scenario Texture", "maker": "scenario", "family": "scenario/texture", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8445, "dossier_id": "scenario/upscale/scenario-flux-upscale", "name": "Scenario Flux Upscale", "maker": "scenario", "family": "scenario/upscale", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8446, "dossier_id": "scenario/upscale/scenario-skybox-upscale", "name": "Scenario Skybox Upscale", "maker": "scenario", "family": "scenario/upscale", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8447, "dossier_id": "scenario/upscale/scenario-texture-upscale", "name": "Scenario Texture Upscale", "maker": "scenario", "family": "scenario/upscale", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Upscale seamlessly tileable textures up to 8x with FLUX. Add detail without breaking tiling. Choose precise, balanced, or creative modes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8448, "dossier_id": "scenario/upscale/scenario-upscale-v3", "name": "Scenario Upscale V3", "maker": "scenario", "family": "scenario/upscale", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8449, "dossier_id": "scu/hongyi/huaxi-hongyi", "name": "\u534e\u897f\u9ec9\u533b", "maker": "scu", "family": "scu/hongyi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8450, "dossier_id": "scu/hxdental/hxdental-pathai", "name": "HXDental-PathAI", "maker": "scu", "family": "scu/hxdental", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8451, "dossier_id": "scut/bianque/bianque-1-0", "name": "BianQue-1.0", "maker": "scut", "family": "scut/bianque", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8452, "dossier_id": "scut/dstc11/dstc11-simmc2-1", "name": "DSTC11 SIMMC 2.1 SCUT-BDS", "maker": "scut", "family": "scut/dstc11", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8453, "dossier_id": "scut/padt/padt-ovd-3b", "name": "PaDT-OVD-3B", "maker": "scut", "family": "scut/padt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8454, "dossier_id": "scut/padt/padt-ovd-7b", "name": "PaDT-OVD-7B", "maker": "scut", "family": "scut/padt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8455, "dossier_id": "scut/padt/padt-pro-7b", "name": "PaDT-Pro-7B", "maker": "scut", "family": "scut/padt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8456, "dossier_id": "scut/padt/padt-rec-3b", "name": "PaDT-REC-3B", "maker": "scut", "family": "scut/padt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8457, "dossier_id": "scut/padt/padt-rec-7b", "name": "PaDT-REC-7B", "maker": "scut", "family": "scut/padt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8458, "dossier_id": "scut/padt/padt-ric-3b", "name": "PaDT-RIC-3B", "maker": "scut", "family": "scut/padt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8459, "dossier_id": "scut/padt/padt-ric-7b", "name": "PaDT-RIC-7B", "maker": "scut", "family": "scut/padt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8460, "dossier_id": "scut/soulchat/soulchat", "name": "SoulChat", "maker": "scut", "family": "scut/soulchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8461, "dossier_id": "scut/soulchat/soulchat2-0-baichuan2-7b", "name": "SoulChat2.0-Baichuan2-7B", "maker": "scut", "family": "scut/soulchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8462, "dossier_id": "scut/soulchat/soulchat2-0-glm-4-9b", "name": "SoulChat2.0-glm-4-9b", "maker": "scut", "family": "scut/soulchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8463, "dossier_id": "scut/soulchat/soulchat2-0-internlm2-7b", "name": "SoulChat2.0-internlm2-7b", "maker": "scut", "family": "scut/soulchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8464, "dossier_id": "scut/soulchat/soulchat2-0-llama-3-1-8b", "name": "SoulChat2.0-Llama-3.1-8B", "maker": "scut", "family": "scut/soulchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8465, "dossier_id": "scut/soulchat/soulchat2-0-llama-3-8b", "name": "SoulChat2.0-Llama-3-8B", "maker": "scut", "family": "scut/soulchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8466, "dossier_id": "scut/soulchat/soulchat2-0-qwen2-7b", "name": "SoulChat2.0-Qwen2-7B", "maker": "scut", "family": "scut/soulchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8467, "dossier_id": "scut/soulchat/soulchat2-0-yi-1-5-9b", "name": "SoulChat2.0-Yi-1.5-9B", "maker": "scut", "family": "scut/soulchat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8468, "dossier_id": "sdu/bianjiang/bianjiang", "name": "\u8fb9\u7586\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/bianjiang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8469, "dossier_id": "sdu/gongshu-zhicang/gongshu-zhicang", "name": "\u516c\u6570\u667a\u8231", "maker": "sdu", "family": "sdu/gongshu-zhicang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8470, "dossier_id": "sdu/qilu/qilu-clinical-skills", "name": "\u9f50\u9c81\u4e34\u5e8a\u6280\u80fd\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/qilu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8471, "dossier_id": "sdu/qilu/qilu-jingjie", "name": "\u9f50\u9c81\u00b7\u955c\u754c\u5589\u955c\u8bca\u65ad\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/qilu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8472, "dossier_id": "sdu/qilu/qilu-xinghuo", "name": "\u9f50\u9c81\u00b7\u661f\u706b\u5168\u75c5\u7a0b\u7ba1\u7406\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/qilu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8473, "dossier_id": "sdu/qilu/qilu-xinqing", "name": "\u9f50\u9c81\u00b7\u5fc3\u64ce\u6025\u6027\u80f8\u75db\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/qilu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8474, "dossier_id": "sdu/sdu-law/sdu-law", "name": "SDU\u6cd5\u5f8b\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/sdu-law", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8475, "dossier_id": "sdu/sdu-llm/sdu-llm", "name": "SDU\u5927\u6a21\u578b", "maker": "sdu", "family": "sdu/sdu-llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8476, "dossier_id": "sensetime/embedding/piccolo-base-zh", "name": "Piccolo-base-zh", "maker": "sensetime", "family": "sensetime/embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese retrieval and semantic search. Use the prompts \u67e5\u8be2 and \u7ed3\u679c.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8477, "dossier_id": "sensetime/embedding/piccolo-large-zh", "name": "Piccolo-large-zh", "maker": "sensetime", "family": "sensetime/embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese retrieval. Prompts differ from v2, which uses none.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8478, "dossier_id": "sensetime/embedding/piccolo-large-zh-v2", "name": "Piccolo-large-zh-v2", "maker": "sensetime", "family": "sensetime/embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General Chinese sentence embeddings for retrieval, clustering, classification, and similarity, without a prompt prefix.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8479, "dossier_id": "sensetime/looped-dit/looped-dit-b16", "name": "Looped-DiT-B/16", "maker": "sensetime", "family": "sensetime/looped-dit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation at a very small parameter count. The paper reports the main benchmark table on B/16.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8480, "dossier_id": "sensetime/looped-dit/looped-dit-b32", "name": "Looped-DiT-B/32", "maker": "sensetime", "family": "sensetime/looped-dit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image research checkpoint for the looping analysis.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8481, "dossier_id": "sensetime/miaohua/sensemirage", "name": "\u79d2\u753b SenseMirage", "maker": "sensetime", "family": "sensetime/miaohua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8482, "dossier_id": "sensetime/miaohua/sensemirage-3", "name": "\u79d2\u753b SenseMirage 3.0", "maker": "sensetime", "family": "sensetime/miaohua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-detail text-to-image than the April 2023 model, including mobile content tools paired with SenseChat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8483, "dossier_id": "sensetime/miaohua/sensenova-miaohua-v5", "name": "\u65e5\u65e5\u65b0\u79d2\u753b V5", "maker": "sensetime", "family": "sensetime/miaohua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image with rendered Latin text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8484, "dossier_id": "sensetime/miaohua/sensenova-v6-5-miaohua", "name": "SenseNova V6.5 Miaohua", "maker": "sensetime", "family": "sensetime/miaohua", "context_length": null, "prompt_price_per_m": 0.03, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8485, "dossier_id": "sensetime/seko/seko-talk", "name": "SekoTalk", "maker": "sensetime", "family": "sensetime/seko", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8486, "dossier_id": "sensetime/senseaudio/senseaudio-a1", "name": "SenseAudio-A1", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full-mix audio from a natural-language description, with up to three reference voices at the product level.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8487, "dossier_id": "sensetime/senseaudio/senseaudio-asr-1-5", "name": "SenseAudio-ASR-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 1.8, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General transcription.", "summary_intro": "SenseAudio-ASR-1.5 is a SenseTime SenseAudio speech-recognition model, id senseaudio-asr-1.5-260319. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8488, "dossier_id": "sensetime/senseaudio/senseaudio-asr-deepthink-1-5", "name": "SenseAudio-ASR-DeepThink-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 3.6, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Recognition with extra reasoning, per the table note.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8489, "dossier_id": "sensetime/senseaudio/senseaudio-asr-lite-1-5", "name": "SenseAudio-ASR-Lite-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 0.9, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-cost file transcription.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8490, "dossier_id": "sensetime/senseaudio/senseaudio-asr-pro-1-5", "name": "SenseAudio-ASR-Pro-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 3.6, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-accuracy transcription.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8491, "dossier_id": "sensetime/senseaudio/senseaudio-asr-stream-1-5", "name": "SenseAudio-ASR-Stream-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 3.6, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming realtime transcription. Id suffix 260910 was not confirmed as 10 September 2026.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8492, "dossier_id": "sensetime/senseaudio/senseaudio-image-2-0", "name": "SenseAudio-Image-2.0", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General image generation on SenseAudio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8493, "dossier_id": "sensetime/senseaudio/senseaudio-music-2-0", "name": "SenseAudio-Music-2.0", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lyrics and full songs from prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8494, "dossier_id": "sensetime/senseaudio/senseaudio-s2", "name": "SenseAudio-S2", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 30.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Flagship SenseAudio LLM: 1M context, deep reasoning, and tool use. Max output on the table is 128K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8495, "dossier_id": "sensetime/senseaudio/senseaudio-s2-flash", "name": "SenseAudio-S2-Flash", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 12.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-throughput S2. Table: 256K context, max input 254K, max output 64K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8496, "dossier_id": "sensetime/senseaudio/senseaudio-s2-lite", "name": "SenseAudio-S2-Lite", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": 6.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-cost S2. Same 256K / 254K / 64K window figures as Flash on the captured table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8497, "dossier_id": "sensetime/senseaudio/senseaudio-sfx-1-0", "name": "SenseAudio-Sound-Effect-1.0", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 0.08, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-sound-effect generation.", "summary_intro": "SenseAudio-Sound-Effect-1.0 is SenseTime's sound-effect model, id senseaudio-sfx-1.0-260626. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8498, "dossier_id": "sensetime/senseaudio/senseaudio-tts-1-5", "name": "SenseAudio-TTS-1.5", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": 3.5, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General emotional TTS. SenseNova-TTS-2.0 is the recommended alternative on the Token Plan page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8499, "dossier_id": "sensetime/senseaudio/senseaudio-voice-isolation", "name": "SenseAudio Voice Isolation", "maker": "sensetime", "family": "sensetime/senseaudio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Separate vocals from a mix.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8500, "dossier_id": "sensetime/sensechat/sensechat", "name": "\u5546\u91cf SenseChat 1.0", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8501, "dossier_id": "sensetime/sensechat/sensechat-2", "name": "\u5546\u91cf SenseChat 2.0", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8502, "dossier_id": "sensetime/sensechat/sensechat-3", "name": "\u5546\u91cf SenseChat 3.0", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8503, "dossier_id": "sensetime/sensechat/sensechat-s", "name": "\u5546\u91cf SenseChat S", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device or low-cost deployment of the 2023 SenseChat line, paired with SenseMirage for phone content tools.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8504, "dossier_id": "sensetime/sensechat/sensechat-turbo", "name": "SenseChat-Turbo", "maker": "sensetime", "family": "sensetime/sensechat", "context_length": null, "prompt_price_per_m": 0.3, "completion_price_per_m": 0.6, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast question answering and fine-tuning scenarios, per the model table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8505, "dossier_id": "sensetime/sensenova/sensenova-5o", "name": "\u65e5\u65e5\u65b0 SenseNova 5o", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time voice, text, image, and video interaction, including reading a name badge or describing a scene while talking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8506, "dossier_id": "sensetime/sensenova/sensenova-v6-5-omni", "name": "SenseNova V6.5 Omni", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": null, "prompt_price_per_m": 0.08, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Live voice and video conversation with spoken replies.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8507, "dossier_id": "sensetime/sensenova/sensenova-v6-omni", "name": "SenseNova V6 Omni", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Live speech and video conversation for tutoring, translation, and similar turn-taking. Launch demos included guided math help.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8508, "dossier_id": "sensetime/sensenova/sensenova-v6-pro", "name": "SenseNova V6 Pro", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 9.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8509, "dossier_id": "sensetime/sensenova/sensenova-v6-reasoner", "name": "SenseNova V6 Reasoner", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": 16.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8510, "dossier_id": "sensetime/sensenova/sensenova-v6-turbo", "name": "SenseNova V6 Turbo", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": null, "prompt_price_per_m": 1.5, "completion_price_per_m": 4.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cheaper image-text chat than V6 Pro.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8511, "dossier_id": "sensetime/sensenova/sensenova-v6-video", "name": "SenseNova V6 Video", "maker": "sensetime", "family": "sensetime/sensenova", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Summarize, analyze, and reason over videos up to about 10 minutes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8512, "dossier_id": "sensetime/sensenova-u/sensenova-u1-5-8b-mot", "name": "SenseNova-U1.5-8B-MoT", "maker": "sensetime", "family": "sensetime/sensenova-u", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image generation and editing, visual question answering, and interleaved creation without a separate diffusion VAE.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8513, "dossier_id": "sensetime/sensenova-u/sensenova-u1-8b-mot", "name": "SenseNova-U1-8B-MoT", "maker": "sensetime", "family": "sensetime/sensenova-u", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8514, "dossier_id": "sensetime/sensenova-u/sensenova-u1-8b-mot-infographic", "name": "SenseNova-U1-8B-MoT-Infographic", "maker": "sensetime", "family": "sensetime/sensenova-u", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image infographics plus image editing in the same unified model, including Chinese and English text in the layout.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8515, "dossier_id": "sensetime/sensenova-u/sensenova-u1-8b-mot-interleaved", "name": "SenseNova-U1-8B-MoT-Interleaved", "maker": "sensetime", "family": "sensetime/sensenova-u", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-page mixed image and text generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8516, "dossier_id": "sensetime/sensenova-u/sensenova-u1-a3b-mot", "name": "SenseNova-U1-A3B-MoT", "maker": "sensetime", "family": "sensetime/sensenova-u", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same unified understanding and generation job as U1-8B, at a larger MoE size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8517, "dossier_id": "sensetime/sensenova-u/sensenova-u1-fast", "name": "SenseNova U1 Fast", "maker": "sensetime", "family": "sensetime/sensenova-u", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Infographics, dense charts, and slide-style layouts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8518, "dossier_id": "sensetime/sensenova-u/sensenova-u1-pro", "name": "SenseNova U1 Pro", "maker": "sensetime", "family": "sensetime/sensenova-u", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8519, "dossier_id": "sensetime/sensenova-vision/sensenova-vision-7b-mot", "name": "SenseNova-Vision-7B-MoT", "maker": "sensetime", "family": "sensetime/sensenova-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One checkpoint for structured vision, dense geometry, segmentation, and multi-view geometry, with language-defined variants.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8520, "dossier_id": "sensetime/speech/sensenova-audio-fusion-0603", "name": "SenseNova-Audio-Fusion-0603", "maker": "sensetime", "family": "sensetime/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Short speech, dialogue, and social audio, with mixed timbres and streaming playback.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8521, "dossier_id": "sensetime/speech/sensenova-livetranslate-1-0", "name": "SenseNova-LiveTranslate-1.0", "maker": "sensetime", "family": "sensetime/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Realtime recognition, translation, and spoken playback for meetings and streams.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8522, "dossier_id": "sensetime/speech/sensenova-tts-2-0", "name": "SenseNova-TTS-2.0", "maker": "sensetime", "family": "sensetime/speech", "context_length": null, "prompt_price_per_m": 3.5, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Emotional, multi-style speech, including formula reading. Cloning is described at the platform level via voice_id.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8523, "dossier_id": "servicenow/apriel/apriel-1-5-15b-thinker", "name": "Apriel-1.5-15B-Thinker", "maker": "servicenow", "family": "servicenow/apriel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8524, "dossier_id": "servicenow/apriel/apriel-1-6-15b-thinker", "name": "Apriel-1.6-15B-Thinker", "maker": "servicenow", "family": "servicenow/apriel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8525, "dossier_id": "servicenow/apriel/apriel-13b", "name": "Apriel 13B", "maker": "servicenow", "family": "servicenow/apriel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8526, "dossier_id": "servicenow/apriel/apriel-2-0", "name": "Apriel 2.0", "maker": "servicenow", "family": "servicenow/apriel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8527, "dossier_id": "servicenow/apriel/apriel-h1-15b-thinker-sft", "name": "Apriel-H1-15B-Thinker-SFT", "maker": "servicenow", "family": "servicenow/apriel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8528, "dossier_id": "servicenow/apriel/apriel-nemotron-15b-thinker", "name": "Apriel-Nemotron-15B-Thinker", "maker": "servicenow", "family": "servicenow/apriel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8529, "dossier_id": "servicenow/apriel/superapriel-15b-base", "name": "SuperApriel-15B-Base", "maker": "servicenow", "family": "servicenow/apriel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8530, "dossier_id": "servicenow/apriel/superapriel-15b-instruct", "name": "SuperApriel-15B-Instruct", "maker": "servicenow", "family": "servicenow/apriel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8531, "dossier_id": "seu/faheng/faheng", "name": "\u6cd5\u8861", "maker": "seu", "family": "seu/faheng", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8532, "dossier_id": "seu/wendao/wendao", "name": "\u95ee\u9053", "maker": "seu", "family": "seu/wendao", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8533, "dossier_id": "sfu/cage/cage", "name": "CAGE", "maker": "sfu", "family": "sfu/cage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8534, "dossier_id": "sfu/clibd/clibd", "name": "CLIBD", "maker": "sfu", "family": "sfu/clibd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8535, "dossier_id": "sfu/duoduo-clip/duoduo-clip", "name": "Duoduo CLIP", "maker": "sfu", "family": "sfu/duoduo-clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8536, "dossier_id": "sfu/nuiscene/nuiscene", "name": "NuiScene", "maker": "sfu", "family": "sfu/nuiscene", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8537, "dossier_id": "sfu/omages/omages", "name": "Omages", "maker": "sfu", "family": "sfu/omages", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8538, "dossier_id": "sfu/opd/opdmulti", "name": "OPDMulti", "maker": "sfu", "family": "sfu/opd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8539, "dossier_id": "sfu/semlayoutdiff/semlayoutdiff", "name": "SemLayoutDiff", "maker": "sfu", "family": "sfu/semlayoutdiff", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8540, "dossier_id": "sfu/singapo/singapo", "name": "SINGAPO", "maker": "sfu", "family": "sfu/singapo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8541, "dossier_id": "shanghai-ai-lab/caprl/caprl-3b", "name": "CapRL-3B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/caprl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named caption checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8542, "dossier_id": "shanghai-ai-lab/caprl/caprl-internvl3-5-8b", "name": "CapRL-InternVL3.5-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/caprl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named caption checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8543, "dossier_id": "shanghai-ai-lab/caprl/caprl-video-4b", "name": "CapRL-Video-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/caprl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named video caption checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8544, "dossier_id": "shanghai-ai-lab/chemllm/chemllm-2b", "name": "ChemLLM-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/chemllm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chemistry dialogue: molecule names, captions, and reaction-related questions, while keeping general language ability.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8545, "dossier_id": "shanghai-ai-lab/chemllm/chemllm-7b-chat", "name": "ChemLLM-7B-Chat", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/chemllm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chemistry dialogue: molecule names, captions, and reaction-related questions, while keeping general language ability.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8546, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chemistry dialogue: molecule names, captions, and reaction-related questions, while keeping general language ability.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8547, "dossier_id": "shanghai-ai-lab/docopilot/docopilot-2b", "name": "Docopilot-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/docopilot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named document checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8548, "dossier_id": "shanghai-ai-lab/docopilot/docopilot-8b", "name": "Docopilot-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/docopilot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named document checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8549, "dossier_id": "shanghai-ai-lab/hovle/hovle", "name": "HoVLE", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/hovle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8550, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Choice, score, and yes/no questions about a shared state, with optional images, in one forward pass.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8551, "dossier_id": "shanghai-ai-lab/intern-decision/intern-decision-2b", "name": "Intern-Decision-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-decision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Choice, score, and yes/no questions about a shared state, with optional images, in one forward pass.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8552, "dossier_id": "shanghai-ai-lab/intern-decision/intern-decision-4b", "name": "Intern-Decision-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-decision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Choice, score, and yes/no questions about a shared state, with optional images, in one forward pass.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8553, "dossier_id": "shanghai-ai-lab/intern-s/intern-memdec-4b", "name": "Intern-MemDec-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named memory checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8554, "dossier_id": "shanghai-ai-lab/intern-s/intern-s1", "name": "Intern-S1", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General multimodal reasoning plus scientific tasks in chemistry, materials, life science, and earth science.", "summary_intro": "Intern-S1 is a scientific multimodal model from Shanghai AI Laboratory (\u4e66\u751f), released 2025-07-26. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8555, "dossier_id": "shanghai-ai-lab/intern-s/intern-s1-mini", "name": "Intern-S1-mini", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller scientific multimodal model for faster deployment.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8556, "dossier_id": "shanghai-ai-lab/intern-s/intern-s1-pro", "name": "Intern-S1-Pro", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Trillion-scale scientific reasoning across chemistry, materials, life science, and earth science, with general multimodal ability.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8557, "dossier_id": "shanghai-ai-lab/intern-s/intern-s2-397b", "name": "Intern-S2-397B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General reasoning, scientific problem solving, and long-horizon general and scientific agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8558, "dossier_id": "shanghai-ai-lab/intern-s/intern-s2-mobius", "name": "Intern-S2-Mobius", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/intern-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named science checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8559, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Professional scientific tasks at 35B, including small-molecule spatial modeling and material crystal generation, plus general reasoning and agents.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8560, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scientific multimodal reasoning and long-horizon agents. The preview is the earlier public 397B checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8561, "dossier_id": "shanghai-ai-lab/internimage/internimage-b", "name": "InternImage-B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification and a general vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8562, "dossier_id": "shanghai-ai-lab/internimage/internimage-g", "name": "InternImage-G", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification and a general vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8563, "dossier_id": "shanghai-ai-lab/internimage/internimage-h", "name": "InternImage-H", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification and a general vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8564, "dossier_id": "shanghai-ai-lab/internimage/internimage-l", "name": "InternImage-L", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification and a general vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8565, "dossier_id": "shanghai-ai-lab/internimage/internimage-s", "name": "InternImage-S", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification and a general vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8566, "dossier_id": "shanghai-ai-lab/internimage/internimage-t", "name": "InternImage-T", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification and a general vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8567, "dossier_id": "shanghai-ai-lab/internimage/internimage-xl", "name": "InternImage-XL", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image classification and a general vision backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8568, "dossier_id": "shanghai-ai-lab/internlm/internlm2-1-8b", "name": "InternLM2-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Further pretrained checkpoint recommended by the lab for most applications.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8569, "dossier_id": "shanghai-ai-lab/internlm/internlm2-1-8b-reward", "name": "InternLM2-1.8B-Reward", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preference reward model for English and Chinese dialogue, writing, code, and math, balanced for helpfulness and harmlessness.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8570, "dossier_id": "shanghai-ai-lab/internlm/internlm2-20b", "name": "InternLM2-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Further pretrained checkpoint recommended by the lab for most applications.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8571, "dossier_id": "shanghai-ai-lab/internlm/internlm2-20b-reward", "name": "InternLM2-20B-Reward", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preference reward model for English and Chinese dialogue, writing, code, and math, balanced for helpfulness and harmlessness.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8572, "dossier_id": "shanghai-ai-lab/internlm/internlm2-5-1-8b", "name": "InternLM2.5-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Further pretrained base model. The lab recommends InternLM2.5 bases for most applications.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8573, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT and online RLHF chat model with instruction following, chat, and function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8574, "dossier_id": "shanghai-ai-lab/internlm/internlm2-5-20b", "name": "InternLM2.5-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Further pretrained base model. The lab recommends InternLM2.5 bases for most applications.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8575, "dossier_id": "shanghai-ai-lab/internlm/internlm2-5-20b-chat", "name": "InternLM2.5-20B-Chat", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT and online RLHF chat model with instruction following, chat, and function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8576, "dossier_id": "shanghai-ai-lab/internlm/internlm2-5-7b", "name": "InternLM2.5-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Further pretrained base model. The lab recommends InternLM2.5 bases for most applications.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8577, "dossier_id": "shanghai-ai-lab/internlm/internlm2-7b", "name": "InternLM2-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Further pretrained checkpoint recommended by the lab for most applications.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8578, "dossier_id": "shanghai-ai-lab/internlm/internlm2-7b-reward", "name": "InternLM2-7B-Reward", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preference reward model for English and Chinese dialogue, writing, code, and math, balanced for helpfulness and harmlessness.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8579, "dossier_id": "shanghai-ai-lab/internlm/internlm2-base-20b", "name": "InternLM2-Base-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Foundation checkpoint for downstream adaptation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8580, "dossier_id": "shanghai-ai-lab/internlm/internlm2-base-7b", "name": "InternLM2-Base-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Foundation checkpoint for downstream adaptation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8581, "dossier_id": "shanghai-ai-lab/internlm/internlm2-chat-1-8b", "name": "InternLM2-Chat-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT plus online RLHF chat model, recommended for instruction following and function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8582, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT-only intermediate released for alignment research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8583, "dossier_id": "shanghai-ai-lab/internlm/internlm2-chat-20b", "name": "InternLM2-Chat-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT plus online RLHF chat model, recommended for instruction following and function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8584, "dossier_id": "shanghai-ai-lab/internlm/internlm2-chat-20b-sft", "name": "InternLM2-Chat-20B-SFT", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT-only intermediate released for alignment research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8585, "dossier_id": "shanghai-ai-lab/internlm/internlm2-chat-7b", "name": "InternLM2-Chat-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT plus online RLHF chat model, recommended for instruction following and function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8586, "dossier_id": "shanghai-ai-lab/internlm/internlm2-chat-7b-sft", "name": "InternLM2-Chat-7B-SFT", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "SFT-only intermediate released for alignment research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8587, "dossier_id": "shanghai-ai-lab/internlm/internlm2-wqx-20b", "name": "InternLM2-WQX-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8588, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8589, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Critic model released with InternLM2.5-Step-Prover.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8590, "dossier_id": "shanghai-ai-lab/internlm-math/internlm2-math-20b", "name": "InternLM2-Math-20B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8591, "dossier_id": "shanghai-ai-lab/internlm-math/internlm2-math-7b", "name": "InternLM2-Math-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8592, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8593, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8594, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8595, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8596, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Informal math reasoning, code-interpreter solving, and Lean formalization or theorem proving.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8597, "dossier_id": "shanghai-ai-lab/internlm-math/internlm2-step-prover", "name": "InternLM2-Step-Prover", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-math", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Lean 4 theorem proving, fine-tuned on Lean-Github and Lean-Workbook.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8598, "dossier_id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer-7b", "name": "InternLM-XComposer-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-image comprehension and composition, plus visual chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8599, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language chat checkpoint paired with the first XComposer release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8600, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-resolution vision-language chat, from 336 pixels up to 4K as described in the paper title.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8601, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8602, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming video and audio interaction system with perception, long memory, and a reasoning module.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8603, "dossier_id": "shanghai-ai-lab/internlm-xcomposer/internlm-xcomposer2-7b", "name": "InternLM-XComposer2-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlm-xcomposer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Free-form text-image composition.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8604, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller vision-language chat model in the XComposer2 line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8605, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language chat checkpoint of XComposer2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8606, "dossier_id": "shanghai-ai-lab/internlumina/internlumina-u2", "name": "InternLumina-U2", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internlumina", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Omni-visual understanding plus image generation and editing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8607, "dossier_id": "shanghai-ai-lab/internomni/internomni", "name": "InternOmni", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internomni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named omni checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8608, "dossier_id": "shanghai-ai-lab/internthinker/internthinker", "name": "InternThinker", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internthinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8609, "dossier_id": "shanghai-ai-lab/internvideo/internvideo", "name": "InternVideo", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video representation and classification.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8610, "dossier_id": "shanghai-ai-lab/internvideo/internvideo2-5-chat-8b", "name": "InternVideo2.5-Chat-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video understanding and chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8611, "dossier_id": "shanghai-ai-lab/internvideo/internvideo2-chat-8b", "name": "InternVideo2-Chat-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video question answering and chat.", "summary_intro": "InternVideo2-Chat-8B is OpenGVLab's video chat model. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8612, "dossier_id": "shanghai-ai-lab/internvideo/internvideo2-stage2-1b", "name": "InternVideo2-Stage2-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General video representation. Dataset-specific stage-1 checkpoints are not separate products.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8613, "dossier_id": "shanghai-ai-lab/internvideo/internvideo2-stage2-6b", "name": "InternVideo2-Stage2-6B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General video representation.", "summary_intro": "InternVideo2 stage-2 6B is the large OpenGVLab video foundation checkpoint. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8614, "dossier_id": "shanghai-ai-lab/internvit/internvit-300m-448", "name": "InternViT-300M-448px", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction for InternVL and related multimodal models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8615, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction for InternVL and related multimodal models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8616, "dossier_id": "shanghai-ai-lab/internvit/internvit-6b-224", "name": "InternViT-6B-224px", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction for InternVL and related multimodal models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8617, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image feature extraction for InternVL and related multimodal models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8618, "dossier_id": "shanghai-ai-lab/internvl/internvl-14b", "name": "InternVL-14B-224px", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8619, "dossier_id": "shanghai-ai-lab/internvl/internvl-chat-v1-1", "name": "InternVL-Chat-V1.1", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8620, "dossier_id": "shanghai-ai-lab/internvl/internvl-chat-v1-2", "name": "InternVL-Chat-V1.2", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8621, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8622, "dossier_id": "shanghai-ai-lab/internvl/internvl-chat-v1-5", "name": "InternVL-Chat-V1.5", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8623, "dossier_id": "shanghai-ai-lab/internvl/internvl2-1b", "name": "InternVL2-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8624, "dossier_id": "shanghai-ai-lab/internvl/internvl2-26b", "name": "InternVL2-26B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8625, "dossier_id": "shanghai-ai-lab/internvl/internvl2-2b", "name": "InternVL2-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8626, "dossier_id": "shanghai-ai-lab/internvl/internvl2-40b", "name": "InternVL2-40B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8627, "dossier_id": "shanghai-ai-lab/internvl/internvl2-4b", "name": "InternVL2-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8628, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-1b", "name": "InternVL2.5-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8629, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-1b-mpo", "name": "InternVL2.5-1B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8630, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-26b", "name": "InternVL2.5-26B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8631, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-26b-mpo", "name": "InternVL2.5-26B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8632, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-2b", "name": "InternVL2.5-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8633, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-2b-mpo", "name": "InternVL2.5-2B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8634, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-38b", "name": "InternVL2.5-38B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8635, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-38b-mpo", "name": "InternVL2.5-38B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8636, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-4b", "name": "InternVL2.5-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8637, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-4b-mpo", "name": "InternVL2.5-4B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8638, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-78b", "name": "InternVL2.5-78B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8639, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-78b-mpo", "name": "InternVL2.5-78B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8640, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-8b", "name": "InternVL2.5-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8641, "dossier_id": "shanghai-ai-lab/internvl/internvl2-5-8b-mpo", "name": "InternVL2.5-8B-MPO", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8642, "dossier_id": "shanghai-ai-lab/internvl/internvl2-8b", "name": "InternVL2-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8643, "dossier_id": "shanghai-ai-lab/internvl/internvl2-llama3-76b", "name": "InternVL2-Llama3-76B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8644, "dossier_id": "shanghai-ai-lab/internvl/internvl3-14b", "name": "InternVL3-14B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8645, "dossier_id": "shanghai-ai-lab/internvl/internvl3-1b", "name": "InternVL3-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8646, "dossier_id": "shanghai-ai-lab/internvl/internvl3-2b", "name": "InternVL3-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8647, "dossier_id": "shanghai-ai-lab/internvl/internvl3-38b", "name": "InternVL3-38B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8648, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-14b", "name": "InternVL3.5-14B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8649, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-1b", "name": "InternVL3.5-1B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8650, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-20b-a4b", "name": "InternVL3.5-20B-A4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8651, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-241b-a28b", "name": "InternVL3.5-241B-A28B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8652, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-2b", "name": "InternVL3.5-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8653, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-30b-a3b", "name": "InternVL3.5-30B-A3B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8654, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-38b", "name": "InternVL3.5-38B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8655, "dossier_id": "shanghai-ai-lab/internvl/internvl3-5-4b", "name": "InternVL3.5-4B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8656, "dossier_id": "shanghai-ai-lab/internvl/internvl3-78b", "name": "InternVL3-78B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8657, "dossier_id": "shanghai-ai-lab/internvl/internvl3-8b", "name": "InternVL3-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8658, "dossier_id": "shanghai-ai-lab/internvl/internvl3-9b", "name": "InternVL3-9B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8659, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8660, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8661, "dossier_id": "shanghai-ai-lab/internvl-u/internvl-u", "name": "InternVL-U", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/internvl-u", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "One small model for multimodal understanding and image generation or editing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8662, "dossier_id": "shanghai-ai-lab/januscoder/januscoder-14b", "name": "JanusCoder-14B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/januscoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named code checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8663, "dossier_id": "shanghai-ai-lab/januscoder/januscoder-8b", "name": "JanusCoder-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/januscoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named code checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8664, "dossier_id": "shanghai-ai-lab/januscoder/januscoder-v-7b", "name": "JanusCoderV-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/januscoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named code vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8665, "dossier_id": "shanghai-ai-lab/januscoder/januscoder-v-8b", "name": "JanusCoderV-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/januscoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named code vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8666, "dossier_id": "shanghai-ai-lab/mono-internvl/mono-internvl-2b", "name": "Mono-InternVL-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/mono-internvl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image and document understanding, visual reasoning, and multimodal chat. Later generations add video and longer visual context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8667, "dossier_id": "shanghai-ai-lab/navil/navil-2b", "name": "NaViL-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/navil", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "summary_intro": "NaViL-2B is a named OpenGVLab model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8668, "dossier_id": "shanghai-ai-lab/navil/navil-9b", "name": "NaViL-9B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/navil", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "summary_intro": "NaViL-9B is a named OpenGVLab model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8669, "dossier_id": "shanghai-ai-lab/polar/polar-1-8b", "name": "POLAR-1.8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/polar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named text reward checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8670, "dossier_id": "shanghai-ai-lab/polar/polar-7b", "name": "POLAR-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/polar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named text reward checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8671, "dossier_id": "shanghai-ai-lab/scalecua/scalecua-32b", "name": "ScaleCUA-32B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/scalecua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named computer use checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8672, "dossier_id": "shanghai-ai-lab/scalecua/scalecua-3b", "name": "ScaleCUA-3B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/scalecua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named computer use checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8673, "dossier_id": "shanghai-ai-lab/scalecua/scalecua-7b", "name": "ScaleCUA-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/scalecua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named computer use checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8674, "dossier_id": "shanghai-ai-lab/sdlm/sdlm-32b-d4", "name": "SDLM-32B-D4", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/sdlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named text checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8675, "dossier_id": "shanghai-ai-lab/sdlm/sdlm-3b-d4", "name": "SDLM-3B-D4", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/sdlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named text checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8676, "dossier_id": "shanghai-ai-lab/sdlm/sdlm-3b-d8", "name": "SDLM-3B-D8", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/sdlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named text checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8677, "dossier_id": "shanghai-ai-lab/spark/spark-vl-7b", "name": "Spark-VL-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/spark", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named video checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8678, "dossier_id": "shanghai-ai-lab/videochat/videochat-flash-2b", "name": "VideoChat-Flash-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Efficient video understanding.", "summary_intro": "VideoChat-Flash-2B is the smaller OpenGVLab video chat model using Qwen2.5-2B. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8679, "dossier_id": "shanghai-ai-lab/videochat/videochat-flash-7b", "name": "VideoChat-Flash-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Efficient video understanding and chat.", "summary_intro": "VideoChat-Flash-7B is an OpenGVLab long-video chat model using a Qwen2-7B language model. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8680, "dossier_id": "shanghai-ai-lab/videochat/videochat-r1-5-7b", "name": "VideoChat-R1.5-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video reasoning.", "summary_intro": "VideoChat-R1.5-7B is the later OpenGVLab video reasoning checkpoint. Collection date 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8681, "dossier_id": "shanghai-ai-lab/videochat/videochat-r1-7b", "name": "VideoChat-R1-7B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video reasoning.", "summary_intro": "VideoChat-R1-7B is a named OpenGVLab video reasoning checkpoint. Collection date 2026-10-15. The card body was not re-read.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8682, "dossier_id": "shanghai-ai-lab/videochat/videochat2", "name": "VideoChat2", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/videochat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video conversation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8683, "dossier_id": "shanghai-ai-lab/visualprm/visualprm-8b", "name": "VisualPRM-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/visualprm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named vision reward checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8684, "dossier_id": "shanghai-ai-lab/vlaser/vlaser-2b", "name": "Vlaser-2B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/vlaser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "summary_intro": "Vlaser-2B is a named OpenGVLab model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8685, "dossier_id": "shanghai-ai-lab/vlaser/vlaser-8b", "name": "Vlaser-8B", "maker": "shanghai-ai-lab", "family": "shanghai-ai-lab/vlaser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named vision checkpoint released by Shanghai AI Laboratory's InternLM or OpenGVLab organization.", "summary_intro": "Vlaser-8B is a named OpenGVLab model. Collection date 2026-10-15. Specs below are limited to what the organization listing shows.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8686, "dossier_id": "shanghaitech/tace/tace-oam-7m", "name": "TACE-OAM-7M", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8687, "dossier_id": "shanghaitech/tace/tace-oam-l", "name": "TACE-OAM-L", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8688, "dossier_id": "shanghaitech/tace/tace-oam-rra-preview", "name": "TACE-OAM-RRA-Preview", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8689, "dossier_id": "shanghaitech/tace/tace-omat24-7m", "name": "TACE-OMat24-7M", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8690, "dossier_id": "shanghaitech/tace/tace-omat24-l", "name": "TACE-OMat24-L", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8691, "dossier_id": "shanghaitech/tace/tace-omat24-rra-1-0", "name": "TACE-OMat24-RRA-1.0", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8692, "dossier_id": "shanghaitech/tace/tace-v1-les-reico-5-pdagcho", "name": "TACE-v1-LES-REICO-5-PdAgCHO", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8693, "dossier_id": "shanghaitech/tace/tace-v1-oam-m", "name": "TACE-v1-OAM-M", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8694, "dossier_id": "shanghaitech/tace/tace-v1-omat24-m", "name": "TACE-v1-OMat24-M", "maker": "shanghaitech", "family": "shanghaitech/tace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8695, "dossier_id": "shanghaitech/tece/tece-oam-rra-1-0", "name": "TECE-OAM-RRA-1.0", "maker": "shanghaitech", "family": "shanghaitech/tece", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8696, "dossier_id": "shanghaitech/tece/tece-omat24-rra-1-0", "name": "TECE-OMat24-RRA-1.0", "maker": "shanghaitech", "family": "shanghaitech/tece", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8697, "dossier_id": "shanghaitech/unicas/unicas", "name": "UniCAS", "maker": "shanghaitech", "family": "shanghaitech/unicas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8698, "dossier_id": "sheffield/annie/annie", "name": "ANNIE", "maker": "sheffield", "family": "sheffield/annie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8699, "dossier_id": "sheffield/bio-yodie/bio-yodie", "name": "Bio-YODIE", "maker": "sheffield", "family": "sheffield/bio-yodie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8700, "dossier_id": "sheffield/cymrie/cymrie", "name": "CYMRIE", "maker": "sheffield", "family": "sheffield/cymrie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8701, "dossier_id": "sheffield/gate-hate/gate-hate", "name": "GATE Hate", "maker": "sheffield", "family": "sheffield/gate-hate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8702, "dossier_id": "sheffield/hyperpartisan/hyperpartisan", "name": "Hyperpartisan scorer", "maker": "sheffield", "family": "sheffield/hyperpartisan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The service writes one annotation over the whole text with a feature hyperpartisan_probability between 0 and 1.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8703, "dossier_id": "sheffield/twitie/twitie", "name": "TwitIE", "maker": "sheffield", "family": "sheffield/twitie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8704, "dossier_id": "sheffield/yodie/yodie", "name": "YODIE", "maker": "sheffield", "family": "sheffield/yodie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8705, "dossier_id": "shengshu/audio/audio1-0", "name": "Vidu Audio 1.0", "maker": "shengshu", "family": "shengshu/audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8706, "dossier_id": "shengshu/vidu/vidu", "name": "Vidu", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8707, "dossier_id": "shengshu/vidu/vidu-1-5", "name": "Vidu 1.5", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8708, "dossier_id": "shengshu/vidu/vidu-2-0", "name": "Vidu 2.0", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8709, "dossier_id": "shengshu/vidu/viduq1", "name": "Vidu Q1", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8710, "dossier_id": "shengshu/vidu/viduq1-classic", "name": "Vidu Q1 Classic", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8711, "dossier_id": "shengshu/vidu/viduq2", "name": "Vidu Q2", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8712, "dossier_id": "shengshu/vidu/viduq2-pro", "name": "Vidu Q2 Pro", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8713, "dossier_id": "shengshu/vidu/viduq2-pro-fast", "name": "Vidu Q2 Pro Fast", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8714, "dossier_id": "shengshu/vidu/viduq2-turbo", "name": "Vidu Q2 Turbo", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8715, "dossier_id": "shengshu/vidu/viduq3", "name": "Vidu Q3", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8716, "dossier_id": "shengshu/vidu/viduq3-ad", "name": "Vidu Q3 Ad", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8717, "dossier_id": "shengshu/vidu/viduq3-drama", "name": "Vidu Q3 Drama", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8718, "dossier_id": "shengshu/vidu/viduq3-mix", "name": "Vidu Q3 Mix", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8719, "dossier_id": "shengshu/vidu/viduq3-pro", "name": "Vidu Q3 Pro", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8720, "dossier_id": "shengshu/vidu/viduq3-pro-fast", "name": "Vidu Q3 Pro Fast", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8721, "dossier_id": "shengshu/vidu/viduq3-turbo", "name": "Vidu Q3 Turbo", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8722, "dossier_id": "shengshu/vidu/viduq4-preview", "name": "Vidu Q4 Preview", "maker": "shengshu", "family": "shengshu/vidu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8723, "dossier_id": "shengshu/vidu-s/vidu-s1", "name": "Vidu S1", "maker": "shengshu", "family": "shengshu/vidu-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8724, "dossier_id": "shengshu/vidu-s/vidu-s2-avatar", "name": "Vidu S2-Avatar", "maker": "shengshu", "family": "shengshu/vidu-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8725, "dossier_id": "shengshu/vidu-s/vidu-s2-editing", "name": "Vidu S2-Editing", "maker": "shengshu", "family": "shengshu/vidu-s", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8726, "dossier_id": "sherbrooke/tractoracle/tractoracle-irt", "name": "TractOracle-IRT", "maker": "sherbrooke", "family": "sherbrooke/tractoracle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8727, "dossier_id": "sherbrooke/tractoracle/tractoracle-net", "name": "TractOracle-Net", "maker": "sherbrooke", "family": "sherbrooke/tractoracle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8728, "dossier_id": "sherbrooke/tractoracle/tractoracle-rl", "name": "TractOracle-RL", "maker": "sherbrooke", "family": "sherbrooke/tractoracle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8729, "dossier_id": "sjtu/davinci/davinci-magihuman", "name": "daVinci-MagiHuman", "maker": "sjtu", "family": "sjtu/davinci", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Joint speech and video generation with expressive faces and synced audio, in Mandarin, Cantonese, English, Japanese, Korean, German, and French.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8730, "dossier_id": "sjtu/livetalk/livetalk-1-3b", "name": "LiveTalk-1.3B", "maker": "sjtu", "family": "sjtu/livetalk", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8731, "dossier_id": "sjtu/rst/rst-all-11b", "name": "RST-All-11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8732, "dossier_id": "sjtu/rst/rst-fact-retrieval-11b", "name": "RST Fact Retrieval 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized for knowledge-heavy tasks, information extraction, and factual checking, using WordNet, wikiHow, Wikidata, and Papers with Code signals.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8733, "dossier_id": "sjtu/rst/rst-gaokao-cloze-11b", "name": "RST Gaokao Cloze 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cloze model trained on manually crafted cloze data. The card recommends it for general cloze filling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8734, "dossier_id": "sjtu/rst/rst-gaokao-rc-11b", "name": "RST Gaokao Reading Comprehension 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8735, "dossier_id": "sjtu/rst/rst-gaokao-writing-11b", "name": "RST Gaokao Writing 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8736, "dossier_id": "sjtu/rst/rst-information-extraction-11b", "name": "RST Information Extraction 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized for named entity recognition, relation extraction, and other IE in news and scientific text.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8737, "dossier_id": "sjtu/rst/rst-intent-detection-11b", "name": "RST Intent Detection 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized for intent prediction and event prediction from wikiHow goal-step relations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8738, "dossier_id": "sjtu/rst/rst-natural-language-inference-11b", "name": "RST Natural Language Inference 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8739, "dossier_id": "sjtu/rst/rst-sentiment-classification-11b", "name": "RST Sentiment Classification 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized for sentiment and emotion classification using Rotten Tomatoes and Wikipedia sentiment signals.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8740, "dossier_id": "sjtu/rst/rst-summarization-11b", "name": "RST Summarization 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8741, "dossier_id": "sjtu/rst/rst-temporal-reasoning-11b", "name": "RST Temporal Reasoning 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized for temporal reasoning, relation extraction, and event extraction, using DailyMail temporal information and wikiHow procedures.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8742, "dossier_id": "sjtu/rst/rst-topic-classification-11b", "name": "RST Topic Classification 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized for general text classification using DailyMail, arXiv, wikiHow, and Wikipedia category signals.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8743, "dossier_id": "sjtu/rst/rst-word-sense-disambiguation-11b", "name": "RST Word Sense Disambiguation 11B", "maker": "sjtu", "family": "sjtu/rst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Specialized for word sense, part of speech, general IE, and commonsense reasoning from WordNet signals.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8744, "dossier_id": "sk-telecom/a-x/a-x-3-0", "name": "A.X 3.0", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X 3.0 is a a-x model from SK Telecom. Status on 2026-10-15: deprecated. Confidence: medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8745, "dossier_id": "sk-telecom/a-x/a-x-3-0-light", "name": "A.X 3.0 Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X 3.0 Light is a a-x model from SK Telecom. Status on 2026-10-15: deprecated. Confidence: medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8746, "dossier_id": "sk-telecom/a-x/a-x-3-1-light", "name": "A.X 3.1 Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X 3.1 Light is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8747, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision-language model for Korean business documents, charts, handwriting, and images. SKT built it on A.X 4.0 Light.", "summary_intro": "A.X 4.0 VL Light is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8748, "dossier_id": "sk-telecom/a-x/a-x-4-1", "name": "A.X 4.1", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X 4.1 is a a-x model from SK Telecom. Status on 2026-10-15: announced. Confidence: medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8749, "dossier_id": "sk-telecom/a-x/a-x-k2-alm", "name": "A.X K2 ALM", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X K2 ALM is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8750, "dossier_id": "sk-telecom/a-x/a-x-k2-dspark", "name": "A.X K2 DSpark", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X K2 DSpark is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8751, "dossier_id": "sk-telecom/a-x/a-x-k2-light", "name": "A.X K2 Light", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X K2 Light is a a-x model from SK Telecom. Status on 2026-10-15: preview. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8752, "dossier_id": "sk-telecom/a-x/a-x-k2-raon-speech", "name": "A.X K2 Raon-Speech", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X K2 Raon-Speech is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8753, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "A.X K2 VL Light-Preview is a a-x model from SK Telecom. Status on 2026-10-15: preview. Confidence: medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8754, "dossier_id": "sk-telecom/a-x/a-x-ve", "name": "A.X VE", "maker": "sk-telecom", "family": "sk-telecom/a-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image encoder for a future A.X vision-language model. It outputs patch features, not captions.", "summary_intro": "A.X VE is a a-x model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8755, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8756, "dossier_id": "sk-telecom/kobart/kobart-base-v1", "name": "KoBART", "maker": "sk-telecom", "family": "sk-telecom/kobart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "KoBART is a kobart model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8757, "dossier_id": "sk-telecom/kogpt2/kogpt2-base-v2", "name": "KoGPT2", "maker": "sk-telecom", "family": "sk-telecom/kogpt2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "KoGPT2 is a kogpt2 model from SK Telecom. Status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8758, "dossier_id": "skku/contra-il6/contra-il6", "name": "CONTRA-IL6", "maker": "skku", "family": "skku/contra-il6", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8759, "dossier_id": "skku/mulaqua/mulaqua", "name": "Mulaqua", "maker": "skku", "family": "skku/mulaqua", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8760, "dossier_id": "skku/sorbformer/sorbformer", "name": "SorbFormer", "maker": "skku", "family": "skku/sorbformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8761, "dossier_id": "skku/xbittert5/xbittert5-640", "name": "xBitterT5-640", "maker": "skku", "family": "skku/xbittert5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8762, "dossier_id": "skku/xbittert5/xbittert5-720", "name": "xBitterT5-720", "maker": "skku", "family": "skku/xbittert5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8763, "dossier_id": "skydio/autonomy/airspace-assurance", "name": "Skydio Airspace Assurance System", "maker": "skydio", "family": "skydio/autonomy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8764, "dossier_id": "skydio/autonomy/foresight", "name": "Skydio Foresight", "maker": "skydio", "family": "skydio/autonomy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8765, "dossier_id": "skydio/autonomy/pathfinder", "name": "Skydio Pathfinder", "maker": "skydio", "family": "skydio/autonomy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8766, "dossier_id": "skydio/autonomy/skydio-autonomy", "name": "Skydio Autonomy", "maker": "skydio", "family": "skydio/autonomy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8767, "dossier_id": "skydio/pilot/deep-neural-pilot", "name": "Deep Neural Pilot", "maker": "skydio", "family": "skydio/pilot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8768, "dossier_id": "skydio/stereo/gc-net", "name": "GC-Net", "maker": "skydio", "family": "skydio/stereo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8769, "dossier_id": "skydio/vision/nightsense", "name": "Skydio NightSense", "maker": "skydio", "family": "skydio/vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8770, "dossier_id": "skydio/vision/shadow", "name": "Skydio Shadow", "maker": "skydio", "family": "skydio/vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8771, "dossier_id": "skydio/vision/spatial-ai-engine", "name": "Skydio Spatial AI Engine", "maker": "skydio", "family": "skydio/vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8772, "dossier_id": "skydio/vision/visual-navigator", "name": "Skydio Visual Navigator", "maker": "skydio", "family": "skydio/vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8773, "dossier_id": "snowflake/arctic-awm/arctic-awm-14b", "name": "Arctic-AWM-14B", "maker": "snowflake", "family": "snowflake/arctic-awm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8774, "dossier_id": "snowflake/arctic-awm/arctic-awm-4b", "name": "Arctic-AWM-4B", "maker": "snowflake", "family": "snowflake/arctic-awm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8775, "dossier_id": "snowflake/arctic-awm/arctic-awm-8b", "name": "Arctic-AWM-8B", "maker": "snowflake", "family": "snowflake/arctic-awm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8776, "dossier_id": "snowflake/arctic-embed/e5-base-arctic-finetune", "name": "e5-base Arctic fine-tune", "maker": "snowflake", "family": "snowflake/arctic-embed", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8777, "dossier_id": "snowflake/arctic-speculator/arctic-lstm-speculator-gpt-oss-120b", "name": "Arctic LSTM Speculator for gpt-oss 120B", "maker": "snowflake", "family": "snowflake/arctic-speculator", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8778, "dossier_id": "snowflake/arctic-speculator/arctic-lstm-speculator-gpt-oss-20b", "name": "Arctic LSTM Speculator for gpt-oss 20B", "maker": "snowflake", "family": "snowflake/arctic-speculator", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8779, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8780, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8781, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8782, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8783, "dossier_id": "snowflake/arctic-speculator/arctic-lstm-speculator-qwen3-32b-bird", "name": "Arctic LSTM Speculator for Qwen3 32B, BIRD", "maker": "snowflake", "family": "snowflake/arctic-speculator", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8784, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8785, "dossier_id": "snowflake/arctic-text2sql/arctic-excot-32b", "name": "Arctic-ExCoT 32B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8786, "dossier_id": "snowflake/arctic-text2sql/arctic-excot-70b", "name": "Arctic-ExCoT 70B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8787, "dossier_id": "snowflake/arctic-text2sql/arctic-text2sql-r1-14b", "name": "Arctic-Text2SQL-R1 14B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8788, "dossier_id": "snowflake/arctic-text2sql/arctic-text2sql-r1-32b", "name": "Arctic-Text2SQL-R1 32B", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8789, "dossier_id": "snowflake/arctic-text2sql/arctic-text2sql-r1-5", "name": "Arctic-Text2SQL-R1.5", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8790, "dossier_id": "snowflake/arctic-text2sql/arctic-text2sql-r2", "name": "Arctic-Text2SQL-R2", "maker": "snowflake", "family": "snowflake/arctic-text2sql", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8791, "dossier_id": "snu/dynin/dynin-robotics", "name": "Dynin-Robotics", "maker": "snu", "family": "snu/dynin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8792, "dossier_id": "snu/kr-bert/kr-bert-kosac", "name": "KR-BERT-KOSAC", "maker": "snu", "family": "snu/kr-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8793, "dossier_id": "softbank/hikari/hikari-medium", "name": "Hikari-medium", "maker": "softbank", "family": "softbank/hikari", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Simultaneous English transcription and English-to-Japanese, Russian, or German translation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8794, "dossier_id": "softbank/ltm/large-telecom-model", "name": "Large Telecom Model", "maker": "softbank", "family": "softbank/ltm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mobile-network operations. The announced example is predicting communication quality during large events.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8795, "dossier_id": "softbank/med-sarashina/med-sarashina", "name": "Med-Sarashina", "maker": "softbank", "family": "softbank/med-sarashina", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8796, "dossier_id": "softbank/modernbert-ja/modernbert-ja-1-4b", "name": "ModernBERT-Ja-1.4B", "maker": "softbank", "family": "softbank/modernbert-ja", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8797, "dossier_id": "softbank/nest/nest-ja-0-1b", "name": "nest-ja-0.1b", "maker": "softbank", "family": "softbank/nest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Frozen audio features or initialization for downstream Japanese speech models. It is not a speech-to-text product by itself.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8798, "dossier_id": "softbank/nest/nest-ja-0-6b", "name": "nest-ja-0.6b", "maker": "softbank", "family": "softbank/nest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Frozen audio features or initialization for downstream Japanese speech models. It is not a speech-to-text product by itself.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8799, "dossier_id": "softbank/sarashina-embedding/sarashina2-embedding", "name": "Sarashina2 embedding", "maker": "softbank", "family": "softbank/sarashina-embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The blog treats it as the previous retrieval embedding, outperformed or replaced on dimensionality by Sarashina3 embedding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8800, "dossier_id": "softbank/sarashina-mini/sarashina-mini", "name": "Sarashina mini", "maker": "softbank", "family": "softbank/sarashina-mini", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8801, "dossier_id": "softbank/sarashina2-2/sarashina2-2-vision-3b", "name": "Sarashina2.2-Vision-3B", "maker": "softbank", "family": "softbank/sarashina2-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image understanding, including Japanese document and slide questions, plus English document VQA.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8802, "dossier_id": "softbank/sarashina3/sarashina3-translate", "name": "Sarashina3 translate", "maker": "softbank", "family": "softbank/sarashina3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8803, "dossier_id": "sogou/speech-translation/sogou-tiic", "name": "Sogou-TIIC Speech Translation System", "maker": "sogou", "family": "sogou/speech-translation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8804, "dossier_id": "sogou/tongchuan/snmt", "name": "SNMT\uff08\u641c\u72d7\u540c\u4f20\uff09", "maker": "sogou", "family": "sogou/tongchuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8805, "dossier_id": "sogou/translate/sogou-translate", "name": "\u641c\u72d7\u7ffb\u8bd1\uff08Sogou Translate\uff09", "maker": "sogou", "family": "sogou/translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8806, "dossier_id": "soniox/stt/stt-async-preview-v1", "name": "Speech-to-Text Async Preview v1", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8807, "dossier_id": "soniox/stt/stt-async-v3", "name": "Speech-to-Text Async v3", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8808, "dossier_id": "soniox/stt/stt-async-v4", "name": "Speech-to-Text Async v4", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8809, "dossier_id": "soniox/stt/stt-async-v5", "name": "Speech-to-Text Async v5", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": 1.5, "completion_price_per_m": 3.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8810, "dossier_id": "soniox/stt/stt-rt-preview-v1", "name": "Speech-to-Text Real-time Preview v1", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8811, "dossier_id": "soniox/stt/stt-rt-preview-v2", "name": "Speech-to-Text Real-time Preview v2", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8812, "dossier_id": "soniox/stt/stt-rt-v3", "name": "Speech-to-Text Real-time v3", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8813, "dossier_id": "soniox/stt/stt-rt-v4", "name": "Speech-to-Text Real-time v4", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8814, "dossier_id": "soniox/stt/stt-rt-v5", "name": "Speech-to-Text Real-time v5", "maker": "soniox", "family": "soniox/stt", "context_length": null, "prompt_price_per_m": 2.0, "completion_price_per_m": 4.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8815, "dossier_id": "soniox/tts/tts-rt-v1", "name": "Text-to-Speech Real-time v1", "maker": "soniox", "family": "soniox/tts", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": 21.5, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8816, "dossier_id": "soniox/tts/tts-rt-v1-preview", "name": "Text-to-Speech Real-time v1 Preview", "maker": "soniox", "family": "soniox/tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8817, "dossier_id": "soniox/tts/tts-rt-v2", "name": "Text-to-Speech Real-time v2", "maker": "soniox", "family": "soniox/tts", "context_length": null, "prompt_price_per_m": 4.0, "completion_price_per_m": 21.5, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8818, "dossier_id": "sony/aki/aki-4b", "name": "AKI-4B", "maker": "sony", "family": "sony/aki", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8819, "dossier_id": "sony/argus/argus", "name": "Argus", "maker": "sony", "family": "sony/argus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8820, "dossier_id": "sony/ctm/ctm", "name": "Consistency Trajectory Model", "maker": "sony", "family": "sony/ctm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8821, "dossier_id": "sony/diff-a-riff/diff-a-riff", "name": "Diff-A-Riff", "maker": "sony", "family": "sony/diff-a-riff", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8822, "dossier_id": "sony/megami/megami", "name": "MEGAMI", "maker": "sony", "family": "sony/megami", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8823, "dossier_id": "sony/mmaudio/mmaudio", "name": "MMAudio", "maker": "sony", "family": "sony/mmaudio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8824, "dossier_id": "sony/mola/mola", "name": "MoLA", "maker": "sony", "family": "sony/mola", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8825, "dossier_id": "sony/pavas/pavas", "name": "PAVAS", "maker": "sony", "family": "sony/pavas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8826, "dossier_id": "sony/soundctm/soundctm", "name": "SoundCTM", "maker": "sony", "family": "sony/soundctm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8827, "dossier_id": "sony/soundreactor/soundreactor", "name": "SoundReactor", "maker": "sony", "family": "sony/soundreactor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8828, "dossier_id": "sony/woosh/woosh-ae", "name": "Woosh-AE", "maker": "sony", "family": "sony/woosh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8829, "dossier_id": "sony/woosh/woosh-clap", "name": "Woosh-CLAP", "maker": "sony", "family": "sony/woosh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8830, "dossier_id": "sony/woosh/woosh-dflow", "name": "Woosh-DFlow", "maker": "sony", "family": "sony/woosh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8831, "dossier_id": "sony/woosh/woosh-dvflow", "name": "Woosh-DVFlow", "maker": "sony", "family": "sony/woosh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8832, "dossier_id": "sony/woosh/woosh-flow", "name": "Woosh-Flow", "maker": "sony", "family": "sony/woosh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8833, "dossier_id": "sony/woosh/woosh-flow-private", "name": "Woosh-Flow Private", "maker": "sony", "family": "sony/woosh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8834, "dossier_id": "sony/woosh/woosh-vflow", "name": "Woosh-VFlow", "maker": "sony", "family": "sony/woosh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8835, "dossier_id": "sorbonne/fennix/fennix-bio1-m", "name": "FeNNix-Bio1(M)", "maker": "sorbonne", "family": "sorbonne/fennix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8836, "dossier_id": "sorbonne/fennix/fennix-bio1-s", "name": "FeNNix-Bio1(S)", "maker": "sorbonne", "family": "sorbonne/fennix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8837, "dossier_id": "sorbonne/fennix/fennix-op1", "name": "FENNIX-OP1", "maker": "sorbonne", "family": "sorbonne/fennix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8838, "dossier_id": "sorbonne/onlyflow/onlyflow", "name": "OnlyFlow", "maker": "sorbonne", "family": "sorbonne/onlyflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8839, "dossier_id": "speechify/simba/simba-3-0", "name": "Simba 3.0", "maker": "speechify", "family": "speechify/simba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8840, "dossier_id": "speechify/simba/simba-3-2", "name": "Simba 3.2", "maker": "speechify", "family": "speechify/simba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8841, "dossier_id": "speechify/simba/simba-base", "name": "Simba Base", "maker": "speechify", "family": "speechify/simba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8842, "dossier_id": "speechify/simba/simba-dialogue-1-0", "name": "Simba Dialogue 1.0", "maker": "speechify", "family": "speechify/simba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speechify describes a multi-speaker model that renders a speaker-attributed script as one conversation with natural turn-taking.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8843, "dossier_id": "speechify/simba/simba-english", "name": "Simba English", "maker": "speechify", "family": "speechify/simba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8844, "dossier_id": "speechify/simba/simba-multilingual", "name": "Simba Multilingual", "maker": "speechify", "family": "speechify/simba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8845, "dossier_id": "speechify/simba/simba-turbo", "name": "Simba Turbo", "maker": "speechify", "family": "speechify/simba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8846, "dossier_id": "speechmatics/enhanced/enhanced", "name": "Enhanced", "maker": "speechmatics", "family": "speechmatics/enhanced", "context_length": null, "prompt_price_per_m": 0.75, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8847, "dossier_id": "speechmatics/enhanced/enhanced-medical", "name": "Enhanced Medical", "maker": "speechmatics", "family": "speechmatics/enhanced", "context_length": null, "prompt_price_per_m": 0.75, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8848, "dossier_id": "speechmatics/linden/linden-1", "name": "Linden 1", "maker": "speechmatics", "family": "speechmatics/linden", "context_length": null, "prompt_price_per_m": 0.3, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8849, "dossier_id": "speechmatics/melia/melia-1", "name": "Melia 1", "maker": "speechmatics", "family": "speechmatics/melia", "context_length": null, "prompt_price_per_m": 0.24, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8850, "dossier_id": "speechmatics/oak/oak-1", "name": "Oak 1", "maker": "speechmatics", "family": "speechmatics/oak", "context_length": null, "prompt_price_per_m": 0.3, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8851, "dossier_id": "speechmatics/standard/standard", "name": "Standard", "maker": "speechmatics", "family": "speechmatics/standard", "context_length": null, "prompt_price_per_m": 0.45, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8852, "dossier_id": "speechmatics/ursa/ursa", "name": "Ursa", "maker": "speechmatics", "family": "speechmatics/ursa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8853, "dossier_id": "speechmatics/ursa/ursa-2", "name": "Ursa 2", "maker": "speechmatics", "family": "speechmatics/ursa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8854, "dossier_id": "st-andrews/delphinid/delphinid", "name": "delphinID", "maker": "st-andrews", "family": "st-andrews/delphinid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8855, "dossier_id": "stability/deepfloyd/deepfloyd-if", "name": "DeepFloyd IF", "maker": "stability", "family": "stability/deepfloyd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research text-to-image generation with strong prompt and rendered-text behavior, via a three-stage cascade.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8856, "dossier_id": "stability/stable-3d/arbor", "name": "Arbor", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research control of text-to-3D generation with constraint meshes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8857, "dossier_id": "stability/stable-3d/sp4d", "name": "SP4D", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video-to-4D generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8858, "dossier_id": "stability/stable-3d/spar3d", "name": "Stable Point Aware 3D", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-mesh generation with editable backside point clouds.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8859, "dossier_id": "stability/stable-3d/stable-fast-3d", "name": "Stable Fast 3D", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast image-to-mesh generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8860, "dossier_id": "stability/stable-3d/stable-zero123", "name": "Stable Zero123", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate new views of an object from one image, for 3D workflows.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8861, "dossier_id": "stability/stable-3d/sv3d", "name": "Stable Video 3D", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate multi-view video of an object from one image, as a step toward 3D assets.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8862, "dossier_id": "stability/stable-3d/sv4d", "name": "Stable Video 4D", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate novel camera views of a moving object from one video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8863, "dossier_id": "stability/stable-3d/sv4d-2", "name": "Stable Video 4D 2.0", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-fidelity 4D novel-view video from a single object video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8864, "dossier_id": "stability/stable-3d/triposr", "name": "TripoSR", "maker": "stability", "family": "stability/stable-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reconstruct a 3D mesh from a single image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8865, "dossier_id": "stability/stable-audio/same-l", "name": "SAME-L", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Compress and reconstruct stereo audio for Stable Audio 3 training and inference.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8866, "dossier_id": "stability/stable-audio/same-s", "name": "SAME-S", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast stereo audio compression on CPU for the small Stable Audio 3 models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8867, "dossier_id": "stability/stable-audio/stable-audio-1-0", "name": "Stable Audio 1.0", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-music and text-to-sound at a user-specified length, up to the training window.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8868, "dossier_id": "stability/stable-audio/stable-audio-2-0", "name": "Stable Audio 2.0", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Full-length text-to-audio and audio-to-audio music generation up to three minutes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8869, "dossier_id": "stability/stable-audio/stable-audio-2-5", "name": "Stable Audio 2.5", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Enterprise music and sound production with section-level structure and audio editing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8870, "dossier_id": "stability/stable-audio/stable-audio-3-large", "name": "Stable Audio 3.0 Large", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-form commercial music and sound generation through the API or an enterprise deployment.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8871, "dossier_id": "stability/stable-audio/stable-audio-3-medium", "name": "Stable Audio 3.0 Medium", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Longer, more structured music and sound generation on a single GPU-class machine.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8872, "dossier_id": "stability/stable-audio/stable-audio-3-small", "name": "Stable Audio 3.0 Small", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device full music sketches up to two minutes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8873, "dossier_id": "stability/stable-audio/stable-audio-3-small-sfx", "name": "Stable Audio 3.0 Small SFX", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Sound-effect generation on phones and consumer laptops.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8874, "dossier_id": "stability/stable-audio/stable-audio-open", "name": "Stable Audio Open 1.0", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Short music samples, foley, and sound design, including fine-tuning on a user's own audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8875, "dossier_id": "stability/stable-audio/stable-audio-open-small", "name": "Stable Audio Open Small", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device short stereo audio samples.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8876, "dossier_id": "stability/stable-audio/stable-codec-speech", "name": "Stable Codec Speech 16 kHz", "maker": "stability", "family": "stability/stable-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Encode and reconstruct 16 kHz speech.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8877, "dossier_id": "stability/stable-cascade/stable-cascade", "name": "Stable Cascade", "maker": "stability", "family": "stability/stable-cascade", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation with a smaller, easier-to-fine-tune cascade than a single large latent model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8878, "dossier_id": "stability/stable-code/stable-code-3b", "name": "Stable Code 3B", "maker": "stability", "family": "stability/stable-code", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation and completion at 3B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8879, "dossier_id": "stability/stable-code/stable-code-alpha-3b", "name": "Stable Code Alpha 3B", "maker": "stability", "family": "stability/stable-code", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code completion.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8880, "dossier_id": "stability/stable-code/stable-code-instruct-3b", "name": "Stable Code Instruct 3B", "maker": "stability", "family": "stability/stable-code", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following code generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8881, "dossier_id": "stability/stable-code/stable-code-instruct-alpha-3b", "name": "Stable Code Instruct Alpha 3B", "maker": "stability", "family": "stability/stable-code", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-following code generation at 3B.", "summary_intro": "Stable Code Instruct Alpha 3B is the August 2023 instruction tune of Stable Code Alpha. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8882, "dossier_id": "stability/stable-diffusion/control-lora", "name": "Stable Diffusion XL Control LoRAs", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Condition SDXL on structural controls such as edges or depth using LoRA adapters.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8883, "dossier_id": "stability/stable-diffusion/cosxl", "name": "CosXL", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation under the CosXL weight release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8884, "dossier_id": "stability/stable-diffusion/japanese-sdxl", "name": "Japanese Stable Diffusion XL", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-prompt text-to-image generation on an SDXL base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8885, "dossier_id": "stability/stable-diffusion/sd-turbo", "name": "SD-Turbo", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast text-to-image synthesis in one to four steps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8886, "dossier_id": "stability/stable-diffusion/sd-vae-ft", "name": "SD VAE FT (MSE and EMA)", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Replace the original SD 1.x VAE decoder to reduce decoded-image artifacts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8887, "dossier_id": "stability/stable-diffusion/sd-x2-latent-upscaler", "name": "Stable Diffusion x2 Latent Upscaler", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Upscale Stable Diffusion latents by 2x.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8888, "dossier_id": "stability/stable-diffusion/sdxl-0-9", "name": "SDXL 0.9", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research-preview text-to-image at 1024 pixels, optionally refined by a second model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8889, "dossier_id": "stability/stable-diffusion/sdxl-1-0", "name": "Stable Diffusion XL 1.0", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "1024-class text-to-image generation, with an optional refiner pass for detail.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8890, "dossier_id": "stability/stable-diffusion/sdxl-turbo", "name": "SDXL Turbo", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time text-to-image generation in one or a few steps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8891, "dossier_id": "stability/stable-diffusion/sdxl-vae", "name": "SDXL VAE", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Encode and decode SDXL latents with the improved decoder Stability shipped for that line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8892, "dossier_id": "stability/stable-diffusion/stable-diffusion-1-4", "name": "Stable Diffusion 1.4", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate and edit images from text prompts at 512 pixels, the original public Stable Diffusion checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8893, "dossier_id": "stability/stable-diffusion/stable-diffusion-1-5", "name": "Stable Diffusion 1.5", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image and image-to-image generation at 512 pixels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8894, "dossier_id": "stability/stable-diffusion/stable-diffusion-1-6", "name": "Stable Diffusion 1.6", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hosted text-to-image generation on the legacy v1 API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8895, "dossier_id": "stability/stable-diffusion/stable-diffusion-2-0", "name": "Stable Diffusion 2.0", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation at 512 or 768 pixels with the OpenCLIP 2.x stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8896, "dossier_id": "stability/stable-diffusion/stable-diffusion-2-1", "name": "Stable Diffusion 2.1", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation that keeps the 2.x OpenCLIP stack while restoring more of the 1.x prompt behavior.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8897, "dossier_id": "stability/stable-diffusion/stable-diffusion-2-inpainting", "name": "Stable Diffusion 2.0 Inpainting", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fill masked regions of an image from a text prompt using the SD 2.x stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8898, "dossier_id": "stability/stable-diffusion/stable-diffusion-3-5-flash", "name": "Stable Diffusion 3.5 Flash", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast, lower-cost text-to-image generation, including on consumer and mobile-class hardware in the research write-up.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8899, "dossier_id": "stability/stable-diffusion/stable-diffusion-3-5-large", "name": "Stable Diffusion 3.5 Large", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8900, "dossier_id": "stability/stable-diffusion/stable-diffusion-3-5-large-controlnet", "name": "SD 3.5 Large ControlNets", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Guide SD 3.5 Large with blur, edge, or depth controls. Blur is also described for tiled high-resolution upscaling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8901, "dossier_id": "stability/stable-diffusion/stable-diffusion-3-5-large-turbo", "name": "Stable Diffusion 3.5 Large Turbo", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast text-to-image generation with SD 3.5 Large prompt adherence in four steps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8902, "dossier_id": "stability/stable-diffusion/stable-diffusion-3-5-medium", "name": "Stable Diffusion 3.5 Medium", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation that trades some of Large's size for speed and easier local use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8903, "dossier_id": "stability/stable-diffusion/stable-diffusion-3-large", "name": "Stable Diffusion 3 Large", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Hosted high-quality text-to-image generation before SD 3.5 Large replaced it.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8904, "dossier_id": "stability/stable-diffusion/stable-diffusion-3-large-turbo", "name": "Stable Diffusion 3 Large Turbo", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster hosted SD3 text-to-image generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8905, "dossier_id": "stability/stable-diffusion/stable-diffusion-3-medium", "name": "Stable Diffusion 3 Medium", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-weight text-to-image generation that fits consumer GPUs better than the closed SD3 Large API model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8906, "dossier_id": "stability/stable-diffusion/stable-diffusion-x4-upscaler", "name": "Stable Diffusion x4 Upscaler", "maker": "stability", "family": "stability/stable-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Upscale an image by 4x, including as the last stage of DeepFloyd IF.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8907, "dossier_id": "stability/stable-image/conservative-upscaler", "name": "Conservative Upscaler", "maker": "stability", "family": "stability/stable-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faithful upscale to a 4 megapixel output.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8908, "dossier_id": "stability/stable-image/creative-upscaler", "name": "Creative Upscaler", "maker": "stability", "family": "stability/stable-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Prompt-guided creative upscale to 4K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8909, "dossier_id": "stability/stable-image/fast-upscaler", "name": "Fast Upscaler", "maker": "stability", "family": "stability/stable-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cheap, fast 4x upscale for compressed images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8910, "dossier_id": "stability/stable-image/stable-image-core", "name": "Stable Image Core", "maker": "stability", "family": "stability/stable-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Cost-efficient hosted text-to-image generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8911, "dossier_id": "stability/stable-image/stable-image-ultra", "name": "Stable Image Ultra", "maker": "stability", "family": "stability/stable-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Highest-quality hosted text-to-image and image-to-image generation in the Stable Image API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8912, "dossier_id": "stability/stable-lm/ar-stablelm-2", "name": "Arabic Stable LM 2", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic and English text generation in the Stable LM 2 line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8913, "dossier_id": "stability/stable-lm/japanese-stable-clip", "name": "Japanese Stable CLIP", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese image-text embeddings and retrieval.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8914, "dossier_id": "stability/stable-lm/japanese-stable-vlm", "name": "Japanese Stable VLM", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese image captioning and visual question answering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8915, "dossier_id": "stability/stable-lm/japanese-stablelm-2-1-6b", "name": "Japanese Stable LM 2 1.6B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese text generation at the Stable LM 2 1.6B scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8916, "dossier_id": "stability/stable-lm/japanese-stablelm-3b", "name": "Japanese StableLM 3B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small Japanese base and instruct language model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8917, "dossier_id": "stability/stable-lm/japanese-stablelm-alpha-7b", "name": "Japanese StableLM Alpha 7B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese text generation at 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8918, "dossier_id": "stability/stable-lm/stablelm-2-1-6b", "name": "Stable LM 2 1.6B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small multilingual base and chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8919, "dossier_id": "stability/stable-lm/stablelm-2-12b", "name": "Stable LM 2 12B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger open multilingual base and chat model in the Stable LM 2 line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8920, "dossier_id": "stability/stable-lm/stablelm-3b", "name": "StableLM 3B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small open base language model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8921, "dossier_id": "stability/stable-lm/stablelm-alpha-3b", "name": "StableLM Alpha 3B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research text generation and instruction following at a 3B scale.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8922, "dossier_id": "stability/stable-lm/stablelm-alpha-3b-v2", "name": "StableLM Alpha 3B v2", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Updated 3B base language model for research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8923, "dossier_id": "stability/stable-lm/stablelm-alpha-7b", "name": "StableLM Alpha 7B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research text generation and instruction following at 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8924, "dossier_id": "stability/stable-lm/stablelm-alpha-7b-v2", "name": "StableLM Alpha 7B v2", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Updated 7B base language model for research.", "summary_intro": "StableLM Alpha 7B v2 is the August 2023 refreshed 7B base, CC-BY-SA-4.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8925, "dossier_id": "stability/stable-lm/stablelm-zephyr-3b", "name": "StableLM Zephyr 3B", "maker": "stability", "family": "stability/stable-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small English chat model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8926, "dossier_id": "stability/stable-video/four-director", "name": "4Director", "maker": "stability", "family": "stability/stable-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research control of camera and rigid object motion in generated video.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8927, "dossier_id": "stability/stable-video/semantok", "name": "SemanTok", "maker": "stability", "family": "stability/stable-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Research autoregressive video generation with semantic tokens.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8928, "dossier_id": "stability/stable-video/stable-video-diffusion", "name": "Stable Video Diffusion", "maker": "stability", "family": "stability/stable-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-video generation at 14 or 25 frames.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8929, "dossier_id": "stability/stable-video/stable-virtual-camera", "name": "Stable Virtual Camera", "maker": "stability", "family": "stability/stable-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate a video of a new camera path from an input image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8930, "dossier_id": "stanford/alpaca/alpaca-7b", "name": "Alpaca 7B", "maker": "stanford", "family": "stanford/alpaca", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8931, "dossier_id": "stanford/anticipatory-music/autoregressive-music-small-interarrival", "name": "Autoregressive Music Transformer Small (interarrival)", "maker": "stanford", "family": "stanford/anticipatory-music", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8932, "dossier_id": "stanford/glove/glove", "name": "GloVe", "maker": "stanford", "family": "stanford/glove", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8933, "dossier_id": "stellenbosch/asgan/asgan", "name": "AudioStyleGAN", "maker": "stellenbosch", "family": "stellenbosch/asgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8934, "dossier_id": "stellenbosch/knn-vc/knn-vc", "name": "kNN-VC", "maker": "stellenbosch", "family": "stellenbosch/knn-vc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8935, "dossier_id": "stellenbosch/soft-vc/soft-vc", "name": "Soft-VC", "maker": "stellenbosch", "family": "stellenbosch/soft-vc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8936, "dossier_id": "stellenbosch/stargan-zsvc/stargan-zsvc", "name": "StarGAN-ZSVC", "maker": "stellenbosch", "family": "stellenbosch/stargan-zsvc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8937, "dossier_id": "stellenbosch/transfusion/transfusion-small", "name": "TransFusion Small", "maker": "stellenbosch", "family": "stellenbosch/transfusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8938, "dossier_id": "stellenbosch/urhythmic/urhythmic", "name": "Urhythmic", "maker": "stellenbosch", "family": "stellenbosch/urhythmic", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8939, "dossier_id": "stellenbosch/vq-cpc/vq-cpc", "name": "VQ-CPC", "maker": "stellenbosch", "family": "stellenbosch/vq-cpc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8940, "dossier_id": "stellenbosch/zerospeech-vqvae/zerospeech-vqvae", "name": "ZeroSpeech VQ-VAE", "maker": "stellenbosch", "family": "stellenbosch/zerospeech-vqvae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8941, "dossier_id": "stepfun/step/step-r-mini", "name": "Step R-mini", "maker": "stepfun", "family": "stepfun/step", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Those write-ups say it was meant for math, code, and logic while still handling general writing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8942, "dossier_id": "stepfun/step/step-router-v1", "name": "Step Router V1", "maker": "stepfun", "family": "stepfun/step", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8943, "dossier_id": "stepfun/step-3d/step1x-3d", "name": "Step1X-3D", "maker": "stepfun", "family": "stepfun/step-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8944, "dossier_id": "stepfun/step-asr/step-asr", "name": "Step ASR", "maker": "stepfun", "family": "stepfun/step-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Voice input, voice control, and meeting notes.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8945, "dossier_id": "stepfun/step-asr/step-asr-1-1", "name": "Step ASR 1.1", "maker": "stepfun", "family": "stepfun/step-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Offline transcription of audio files.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8946, "dossier_id": "stepfun/step-asr/step-asr-1-1-stream", "name": "Step ASR 1.1 Stream", "maker": "stepfun", "family": "stepfun/step-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Live voice input.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8947, "dossier_id": "stepfun/step-asr/stepaudio-2-5-asr", "name": "StepAudio 2.5 ASR", "maker": "stepfun", "family": "stepfun/step-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Batch transcription, captions, and voice input where a finished transcript can wait for the whole file.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8948, "dossier_id": "stepfun/step-asr/stepaudio-2-5-asr-stream", "name": "StepAudio 2.5 ASR Stream", "maker": "stepfun", "family": "stepfun/step-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Live captions, voice assistants, and meetings that need partial text before the utterance ends.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8949, "dossier_id": "stepfun/step-asr/stepaudio-2-asr-pro", "name": "StepAudio 2 ASR Pro", "maker": "stepfun", "family": "stepfun/step-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger ASR option beside the 4B StepAudio 2.5 models. StepFun does not state a more specific job.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8950, "dossier_id": "stepfun/step-asr/stepaudio-3-asr", "name": "StepAudio 3 ASR", "maker": "stepfun", "family": "stepfun/step-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8951, "dossier_id": "stepfun/step-audio/step-1o-audio", "name": "Step-1o Audio", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": 25.0, "completion_price_per_m": 60.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "StepFun calls it the stable choice for cars, devices, support, and companionship, with tool calls and preset voices.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8952, "dossier_id": "stepfun/step-audio/step-audio-2", "name": "Step-Audio 2", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": 70.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8953, "dossier_id": "stepfun/step-audio/step-audio-2-mini", "name": "Step-Audio 2 Mini", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A cheaper, faster speech conversation model for the same jobs as Step-Audio 2 when full quality is not required.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8954, "dossier_id": "stepfun/step-audio/step-audio-chat", "name": "Step-Audio-Chat", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The README describes multilingual speech conversation with emotion, dialect, rate, and style control, including Cantonese, Sichuanese, and rap as examples.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8955, "dossier_id": "stepfun/step-audio/step-audio-editx", "name": "Step-Audio-EditX", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Expressive audio editing and zero-shot speech, not the hosted StepAudio 3 TTS API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8956, "dossier_id": "stepfun/step-audio/step-audio-r1", "name": "Step-Audio-R1", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speech reasoning: the repo presents it as the model that reasons about audio rather than only transcribing or chatting.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8957, "dossier_id": "stepfun/step-audio/step-audio-r1-1", "name": "Step-Audio-R1.1", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same speech-reasoning job as R1, with the January 2026 weight update.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8958, "dossier_id": "stepfun/step-audio/step-audio-r1-5", "name": "Step-Audio R1.5", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": 105.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8959, "dossier_id": "stepfun/step-audio/stepaudio-2-5-chat", "name": "StepAudio 2.5 Chat", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": 25.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "StepFun says it reads complex meaning plus hesitation and laughter, then answers in text with a configurable persona.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8960, "dossier_id": "stepfun/step-audio/stepaudio-2-5-realtime", "name": "StepAudio 2.5 Realtime", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": 10.0, "completion_price_per_m": 70.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8961, "dossier_id": "stepfun/step-audio/stepaudio-3-chat", "name": "StepAudio 3 Chat", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8962, "dossier_id": "stepfun/step-audio/stepaudio-3-gen", "name": "StepAudio 3 Gen", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8963, "dossier_id": "stepfun/step-audio/stepaudio-3-music", "name": "StepAudio 3 Music", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8964, "dossier_id": "stepfun/step-audio/stepaudio-3-realtime", "name": "StepAudio 3 Realtime", "maker": "stepfun", "family": "stepfun/step-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8965, "dossier_id": "stepfun/step-gui/step-gui", "name": "Step GUI", "maker": "stepfun", "family": "stepfun/step-gui", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8966, "dossier_id": "stepfun/step-image/nextstep-1", "name": "NextStep-1", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image research and generation with continuous tokens rather than a diffusion-only stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8967, "dossier_id": "stepfun/step-image/nextstep-1-1", "name": "NextStep-1.1", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-quality text-to-image than the original NextStep-1 weights.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8968, "dossier_id": "stepfun/step-image/nextstep-1-large", "name": "NextStep-1-Large", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-capacity text-to-image in the NextStep continuous-token line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8969, "dossier_id": "stepfun/step-image/nextstep-1-large-edit", "name": "NextStep-1-Large-Edit", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image editing in the NextStep continuous-token stack, as opposed to Step1X-Edit.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8970, "dossier_id": "stepfun/step-image/step-1x", "name": "Step-1X", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation, as announced. The article does not quote a longer spec.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8971, "dossier_id": "stepfun/step-image/step-1x-medium", "name": "Step-1X Medium", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8972, "dossier_id": "stepfun/step-image/step-2x-large", "name": "Step-2X Large", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image and image-to-image on the open platform and Step Plan.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8973, "dossier_id": "stepfun/step-image/step-image-edit-2", "name": "Step Image Edit 2", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-based image editing on the hosted image API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8974, "dossier_id": "stepfun/step-image/step1x-edit", "name": "Step1X-Edit", "maker": "stepfun", "family": "stepfun/step-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruction-based image editing, with later versions also doing text-to-image and a reasoning edit mode.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8975, "dossier_id": "stepfun/step-prover/stepfun-prover-preview-7b", "name": "StepFun-Prover-Preview-7B", "maker": "stepfun", "family": "stepfun/step-prover", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Formal proving when the 32B checkpoint is too large.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8976, "dossier_id": "stepfun/step-research/step-deepresearch", "name": "Step-DeepResearch", "maker": "stepfun", "family": "stepfun/step-research", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-ended research: plan, search, check, and write a report in one agent loop.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8977, "dossier_id": "stepfun/step-tts/step-audio-tts-3b", "name": "Step-Audio-TTS-3B", "maker": "stepfun", "family": "stepfun/step-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Local text-to-speech in the original Step-Audio stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8978, "dossier_id": "stepfun/step-tts/step-tts-2", "name": "Step TTS 2", "maker": "stepfun", "family": "stepfun/step-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Emotional dubbing and dialogue, including Cantonese, Sichuanese, and Japanese, plus accent-faithful clones for live-commerce style speech.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8979, "dossier_id": "stepfun/step-tts/step-tts-mini", "name": "Step TTS Mini", "maker": "stepfun", "family": "stepfun/step-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Support calls, companionship, and assistants that need expressive speech more than the newest contextual controls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8980, "dossier_id": "stepfun/step-tts/stepaudio-2-5-tts", "name": "StepAudio 2.5 TTS", "maker": "stepfun", "family": "stepfun/step-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8981, "dossier_id": "stepfun/step-tts/stepaudio-3-tts", "name": "StepAudio 3 TTS", "maker": "stepfun", "family": "stepfun/step-tts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "StepFun says it matches human timbre, intonation, rhythm, breath, and pauses, and can laugh, hesitate, stutter, repeat, and self-correct.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8982, "dossier_id": "stepfun/step-video/step-video-t2v", "name": "Step-Video-T2V", "maker": "stepfun", "family": "stepfun/step-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8983, "dossier_id": "stepfun/step-video/step-video-t2v-turbo", "name": "Step-Video-T2V-Turbo", "maker": "stepfun", "family": "stepfun/step-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster text-to-video inference than the base Step-Video-T2V sampler.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8984, "dossier_id": "stepfun/step-video/step-video-ti2v", "name": "Step-Video-TI2V", "maker": "stepfun", "family": "stepfun/step-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-guided image-to-video, with a motion score so the user can ask for more or less movement.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8985, "dossier_id": "stepfun/step-vision/step-1-5v", "name": "Step-1.5V", "maker": "stepfun", "family": "stepfun/step-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8986, "dossier_id": "stepfun/step-vision/step-r1-v-mini", "name": "Step-R1-V-Mini", "maker": "stepfun", "family": "stepfun/step-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The report says the job is precise image perception plus complex reasoning, with ordinary instruction following kept.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8987, "dossier_id": "stepfun/step-vision/step3-vl-10b", "name": "STEP3-VL-10B", "maker": "stepfun", "family": "stepfun/step-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8988, "dossier_id": "stepfun/step-vision/step3-vl-10b-base", "name": "STEP3-VL-10B-Base", "maker": "stepfun", "family": "stepfun/step-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "StepFun publishes it for people who want the 10B multimodal base rather than the chat checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8989, "dossier_id": "stirling/co4/hybrid-base", "name": "Co4 hybrid_base", "maker": "stirling", "family": "stirling/co4", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8990, "dossier_id": "stockmark/nemotron/stockmark-nemotron-3-nano-omni-japandocreader", "name": "Stockmark-Nemotron-3-Nano-Omni-JapanDocReader", "maker": "stockmark", "family": "stockmark/nemotron", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8991, "dossier_id": "stockmark/stockmark-llm/stockmark-13b-instruct", "name": "Stockmark-13b-instruct", "maker": "stockmark", "family": "stockmark/stockmark-llm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Follows a short Japanese instruction. The card's prompt is a plain `", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8992, "dossier_id": "stonybrook/bigcell/bigcell", "name": "BigCell", "maker": "stonybrook", "family": "stonybrook/bigcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released BigCell. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8993, "dossier_id": "stonybrook/bigcell/bigcell-cgm", "name": "BigCell CGM", "maker": "stonybrook", "family": "stonybrook/bigcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "summary_intro": "Stony Brook University released BigCell CGM. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8994, "dossier_id": "stonybrook/dewarpnet/dewarpnet", "name": "DewarpNet", "maker": "stonybrook", "family": "stonybrook/dewarpnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released DewarpNet. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8995, "dossier_id": "stonybrook/lrdm/lrdm", "name": "LRDM", "maker": "stonybrook", "family": "stonybrook/lrdm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released LRDM. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8996, "dossier_id": "stonybrook/pathldm/pathldm", "name": "PathLDM", "maker": "stonybrook", "family": "stonybrook/pathldm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released PathLDM. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8997, "dossier_id": "stonybrook/pixcell/pixcell-1024", "name": "PixCell-1024", "maker": "stonybrook", "family": "stonybrook/pixcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released PixCell-1024. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8998, "dossier_id": "stonybrook/pixcell/pixcell-256", "name": "PixCell-256", "maker": "stonybrook", "family": "stonybrook/pixcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released PixCell-256. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 8999, "dossier_id": "stonybrook/pixcell/pixcell-256-cell-controlnet", "name": "PixCell-256-Cell-ControlNet", "maker": "stonybrook", "family": "stonybrook/pixcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released PixCell-256-Cell-ControlNet. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9000, "dossier_id": "stonybrook/pixcell/pixcell-virtual-staining", "name": "PixCell virtual staining", "maker": "stonybrook", "family": "stonybrook/pixcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released PixCell virtual staining. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9001, "dossier_id": "stonybrook/zoomldm/zoomldm", "name": "ZoomLDM", "maker": "stonybrook", "family": "stonybrook/zoomldm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released ZoomLDM. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9002, "dossier_id": "stonybrook/zoomldm/zoomldm-cdm", "name": "ZoomLDM CDM", "maker": "stonybrook", "family": "stonybrook/zoomldm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Stony Brook University released ZoomLDM CDM. Status is active. This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9003, "dossier_id": "strasbourg/clipper/clipper", "name": "CliPPER", "maker": "strasbourg", "family": "strasbourg/clipper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9004, "dossier_id": "strasbourg/rendezvous/rendezvous", "name": "Rendezvous", "maker": "strasbourg", "family": "strasbourg/rendezvous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9005, "dossier_id": "strasbourg/rendezvous/rit", "name": "Rendezvous in Time", "maker": "strasbourg", "family": "strasbourg/rendezvous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9006, "dossier_id": "strasbourg/s4m/s4m", "name": "S4M", "maker": "strasbourg", "family": "strasbourg/s4m", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9007, "dossier_id": "strasbourg/surge/surge", "name": "SURGE", "maker": "strasbourg", "family": "strasbourg/surge", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9008, "dossier_id": "strasbourg/surgvlp/hecvl", "name": "HecVL", "maker": "strasbourg", "family": "strasbourg/surgvlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9009, "dossier_id": "strasbourg/surgvlp/peskavlp", "name": "PeskaVLP", "maker": "strasbourg", "family": "strasbourg/surgvlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9010, "dossier_id": "strasbourg/surgvlp/surgvlp", "name": "SurgVLP", "maker": "strasbourg", "family": "strasbourg/surgvlp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9011, "dossier_id": "strasbourg/ultrasam/ultrasam", "name": "UltraSam", "maker": "strasbourg", "family": "strasbourg/ultrasam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9012, "dossier_id": "strathclyde/gold/gold", "name": "GOLD", "maker": "strathclyde", "family": "strathclyde/gold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9013, "dossier_id": "stuttgart/gsi/crealnvp-o-al2o3", "name": "cRealNVP O-Al2O3", "maker": "stuttgart", "family": "stuttgart/gsi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9014, "dossier_id": "stuttgart/gsi/cvae-o-al2o3", "name": "cVAE O-Al2O3", "maker": "stuttgart", "family": "stuttgart/gsi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9015, "dossier_id": "suno/chirp/v0", "name": "Chirp v0", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9016, "dossier_id": "suno/chirp/v1", "name": "Chirp v1", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9017, "dossier_id": "suno/chirp/v2", "name": "Suno v2", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9018, "dossier_id": "suno/chirp/v3", "name": "Suno v3", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9019, "dossier_id": "suno/chirp/v3-5", "name": "Suno v3.5", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9020, "dossier_id": "suno/chirp/v3-alpha", "name": "Suno v3 Alpha", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Preview of the v3 song model for paid subscribers. Suno did not publish a separate alpha spec.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9021, "dossier_id": "suno/chirp/v4", "name": "Suno v4", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9022, "dossier_id": "suno/chirp/v4-5", "name": "Suno v4.5", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9023, "dossier_id": "suno/chirp/v4-5-all", "name": "Suno v4.5-all", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9024, "dossier_id": "suno/chirp/v4-5-plus", "name": "Suno v4.5+", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9025, "dossier_id": "suno/chirp/v5", "name": "Suno v5", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9026, "dossier_id": "suno/chirp/v5-5", "name": "Suno v5.5", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9027, "dossier_id": "suno/chirp/v6", "name": "Suno v6", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9028, "dossier_id": "suno/chirp/v6-mini", "name": "Suno v6-mini", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9029, "dossier_id": "suno/chirp/v6-wild", "name": "Suno v6-wild", "maker": "suno", "family": "suno/chirp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9030, "dossier_id": "suno/speech/speech", "name": "Speech", "maker": "suno", "family": "suno/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9031, "dossier_id": "surrey/audioldm/audioldm", "name": "AudioLDM", "maker": "surrey", "family": "surrey/audioldm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9032, "dossier_id": "surrey/audioldm2/audioldm2", "name": "AudioLDM 2", "maker": "surrey", "family": "surrey/audioldm2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9033, "dossier_id": "surrey/audiosr/audiosr", "name": "AudioSR", "maker": "surrey", "family": "surrey/audiosr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9034, "dossier_id": "surrey/epanns/epanns", "name": "E-PANNs", "maker": "surrey", "family": "surrey/epanns", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9035, "dossier_id": "surrey/panns/panns", "name": "PANNs", "maker": "surrey", "family": "surrey/panns", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9036, "dossier_id": "surrey/semanticodec/semanticodec", "name": "SemantiCodec", "maker": "surrey", "family": "surrey/semanticodec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9037, "dossier_id": "sustech/chemahnet/chemahnet", "name": "ChemAHNet", "maker": "sustech", "family": "sustech/chemahnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9038, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pre-training of Gemma 2. The project says to use the IT checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9039, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pre-training of Gemma 2. The project says to use the IT checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9040, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pre-training of Gemma 2. The project says to use the IT checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9041, "dossier_id": "swallow/gpt-oss-swallow/gpt-oss-swallow-120b", "name": "GPT-OSS Swallow 120B", "maker": "swallow", "family": "swallow/gpt-oss-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest Swallow reasoning model. Use the RL checkpoint with reasoning effort medium.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9042, "dossier_id": "swallow/gpt-oss-swallow/gpt-oss-swallow-20b", "name": "GPT-OSS Swallow 20B", "maker": "swallow", "family": "swallow/gpt-oss-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-English reasoning model. Use the RL checkpoint with reasoning effort medium. SFT is experimental.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9043, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9044, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Current small Llama-based Swallow chat model. The project says to use the v0.5 instruct checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9045, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Latest 70B Llama-based Swallow model. Use the instruct checkpoint unless a base model is required.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9046, "dossier_id": "swallow/llama-3-swallow/llama-3-swallow-70b", "name": "Llama 3 Swallow 70B", "maker": "swallow", "family": "swallow/llama-3-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pre-training of Llama 3 70B. Legacy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9047, "dossier_id": "swallow/llama-3-swallow/llama-3-swallow-8b", "name": "Llama 3 Swallow 8B", "maker": "swallow", "family": "swallow/llama-3-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese continual pre-training of Llama 3 8B, with a chat instruct checkpoint. Legacy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9048, "dossier_id": "swallow/medical-swallow/medical-gpt-oss-swallow-120b", "name": "Medical GPT-OSS Swallow 120B", "maker": "swallow", "family": "swallow/medical-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Largest medical Swallow research model, adapted from GPT-OSS Swallow 120B RL.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9049, "dossier_id": "swallow/medical-swallow/medical-qwen3-swallow-30b-a3b", "name": "Medical Qwen3 Swallow 30B-A3B", "maker": "swallow", "family": "swallow/medical-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese clinical-domain Mixture-of-Experts research model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9050, "dossier_id": "swallow/medical-swallow/medical-qwen3-swallow-32b", "name": "Medical Qwen3 Swallow 32B", "maker": "swallow", "family": "swallow/medical-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese clinical-domain dense research model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9051, "dossier_id": "swallow/medical-swallow/medical-qwen3-swallow-8b", "name": "Medical Qwen3 Swallow 8B", "maker": "swallow", "family": "swallow/medical-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese clinical-domain research model. Not for unsupervised medical decisions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9052, "dossier_id": "swallow/qwen3-swallow/qwen3-swallow-30b-a3b", "name": "Qwen3 Swallow 30B-A3B", "maker": "swallow", "family": "swallow/qwen3-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-English reasoning model. Use the RL v0.2 checkpoint with reasoning enabled. SFT and CPT are experimental.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9053, "dossier_id": "swallow/qwen3-swallow/qwen3-swallow-32b", "name": "Qwen3 Swallow 32B", "maker": "swallow", "family": "swallow/qwen3-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-English reasoning model. Use the RL v0.2 checkpoint with reasoning enabled. SFT and CPT are experimental.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9054, "dossier_id": "swallow/qwen3-swallow/qwen3-swallow-8b", "name": "Qwen3 Swallow 8B", "maker": "swallow", "family": "swallow/qwen3-swallow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-English reasoning model. Use the RL v0.2 checkpoint with reasoning enabled. SFT and CPT are experimental.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9055, "dossier_id": "swallow/swallow-ms/swallow-ms-7b", "name": "Swallow-MS 7B", "maker": "swallow", "family": "swallow/swallow-ms", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Japanese-strengthened Mistral 7B for research and commercial use under Apache 2.0. Legacy relative to later Swallow models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9056, "dossier_id": "swallow/swallow-mx/swallow-mx-8x7b", "name": "Swallow-MX 8x7B", "maker": "swallow", "family": "swallow/swallow-mx", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9057, "dossier_id": "sydney/marsupial/marsupial-16s", "name": "Marsupial 16s", "maker": "sydney", "family": "sydney/marsupial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9058, "dossier_id": "sydney/marsupial/marsupial-33s", "name": "Marsupial 33s", "maker": "sydney", "family": "sydney/marsupial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9059, "dossier_id": "sydney/marsupial/marsupial-41s", "name": "Marsupial 41s", "maker": "sydney", "family": "sydney/marsupial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9060, "dossier_id": "sydney/marsupial/marsupial-72l", "name": "Marsupial 72l", "maker": "sydney", "family": "sydney/marsupial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9061, "dossier_id": "sydney/marsupial/marsupial-72s", "name": "Marsupial 72s", "maker": "sydney", "family": "sydney/marsupial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9062, "dossier_id": "sydney/marsupial/marsupial-72s-lures", "name": "Marsupial 72s Lures", "maker": "sydney", "family": "sydney/marsupial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9063, "dossier_id": "sydney/marsupial/marsupial-detector", "name": "Marsupial Detector", "maker": "sydney", "family": "sydney/marsupial", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9064, "dossier_id": "sydney/vitae/vitae-6m", "name": "ViTAE-6M", "maker": "sydney", "family": "sydney/vitae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9065, "dossier_id": "sydney/vitae/vitae-b", "name": "ViTAE-B", "maker": "sydney", "family": "sydney/vitae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9066, "dossier_id": "sydney/vitae/vitae-h", "name": "ViTAE-H", "maker": "sydney", "family": "sydney/vitae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9067, "dossier_id": "sydney/vitae/vitae-l", "name": "ViTAE-L", "maker": "sydney", "family": "sydney/vitae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9068, "dossier_id": "sydney/vitae/vitae-s", "name": "ViTAE-S", "maker": "sydney", "family": "sydney/vitae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9069, "dossier_id": "sydney/vitae/vitae-t", "name": "ViTAE-T", "maker": "sydney", "family": "sydney/vitae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9070, "dossier_id": "sydney/vitaev2/vitaev2-48m", "name": "ViTAEv2-48M", "maker": "sydney", "family": "sydney/vitaev2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9071, "dossier_id": "sydney/vitaev2/vitaev2-b", "name": "ViTAEv2-B", "maker": "sydney", "family": "sydney/vitaev2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9072, "dossier_id": "sydney/vitaev2/vitaev2-s", "name": "ViTAEv2-S", "maker": "sydney", "family": "sydney/vitaev2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9073, "dossier_id": "sydney/vitpose/vitpose-b", "name": "ViTPose-B", "maker": "sydney", "family": "sydney/vitpose", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9074, "dossier_id": "sydney/vitpose/vitpose-g", "name": "ViTPose-G", "maker": "sydney", "family": "sydney/vitpose", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9075, "dossier_id": "sydney/vitpose/vitpose-h", "name": "ViTPose-H", "maker": "sydney", "family": "sydney/vitpose", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9076, "dossier_id": "sydney/vitpose/vitpose-l", "name": "ViTPose-L", "maker": "sydney", "family": "sydney/vitpose", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9077, "dossier_id": "sydney/vitpose/vitpose-s", "name": "ViTPose-S", "maker": "sydney", "family": "sydney/vitpose", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9078, "dossier_id": "sydney/vitpose-plus/vitpose-plus-b", "name": "ViTPose++-B", "maker": "sydney", "family": "sydney/vitpose-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9079, "dossier_id": "sydney/vitpose-plus/vitpose-plus-h", "name": "ViTPose++-H", "maker": "sydney", "family": "sydney/vitpose-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9080, "dossier_id": "sydney/vitpose-plus/vitpose-plus-l", "name": "ViTPose++-L", "maker": "sydney", "family": "sydney/vitpose-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9081, "dossier_id": "sydney/vitpose-plus/vitpose-plus-s", "name": "ViTPose++-S", "maker": "sydney", "family": "sydney/vitpose-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9082, "dossier_id": "synthesia/express/express-1", "name": "Express-1", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9083, "dossier_id": "synthesia/express/express-2", "name": "Express-2", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9084, "dossier_id": "synthesia/express/express-3", "name": "Express-3", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9085, "dossier_id": "synthesia/express/express-animate", "name": "Express-Animate", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9086, "dossier_id": "synthesia/express/express-eval", "name": "Express-Eval", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9087, "dossier_id": "synthesia/express/express-render", "name": "Express-Render", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9088, "dossier_id": "synthesia/express/express-render-turbo", "name": "Express-Render-Turbo", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9089, "dossier_id": "synthesia/express/express-video", "name": "Express-Video", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9090, "dossier_id": "synthesia/express/express-voice", "name": "Express-Voice", "maker": "synthesia", "family": "synthesia/express", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9091, "dossier_id": "synthesia/legacy/legacy", "name": "Legacy avatars (V3 / Classic)", "maker": "synthesia", "family": "synthesia/legacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9092, "dossier_id": "synthesia/personal/personal-from-photo", "name": "Personal avatar from photo", "maker": "synthesia", "family": "synthesia/personal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9093, "dossier_id": "synthesia/personal/personal-from-video", "name": "Personal avatar from video", "maker": "synthesia", "family": "synthesia/personal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9094, "dossier_id": "synthesia/selfie/selfie-avatar", "name": "Selfie Avatars", "maker": "synthesia", "family": "synthesia/selfie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9095, "dossier_id": "synthesia/stock/aaron", "name": "Aaron", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9096, "dossier_id": "synthesia/stock/ace", "name": "Ace", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9097, "dossier_id": "synthesia/stock/ada", "name": "Ada", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9098, "dossier_id": "synthesia/stock/agnes", "name": "Agnes", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9099, "dossier_id": "synthesia/stock/aiden", "name": "Aiden", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9100, "dossier_id": "synthesia/stock/alex", "name": "Alex", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9101, "dossier_id": "synthesia/stock/alex-beta", "name": "Alex (beta)", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9102, "dossier_id": "synthesia/stock/alisha", "name": "Alisha", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9103, "dossier_id": "synthesia/stock/alora", "name": "Alora", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9104, "dossier_id": "synthesia/stock/andy", "name": "Andy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9105, "dossier_id": "synthesia/stock/angela", "name": "Angela", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9106, "dossier_id": "synthesia/stock/anne-marie", "name": "Anne-Marie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9107, "dossier_id": "synthesia/stock/anton", "name": "Anton", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9108, "dossier_id": "synthesia/stock/arielle", "name": "Arielle", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9109, "dossier_id": "synthesia/stock/arnold", "name": "Arnold", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9110, "dossier_id": "synthesia/stock/ashley", "name": "Ashley", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9111, "dossier_id": "synthesia/stock/ava", "name": "Ava", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9112, "dossier_id": "synthesia/stock/axel", "name": "Axel", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9113, "dossier_id": "synthesia/stock/barry", "name": "Barry", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9114, "dossier_id": "synthesia/stock/billie", "name": "Billie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9115, "dossier_id": "synthesia/stock/blake", "name": "Blake", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9116, "dossier_id": "synthesia/stock/bridget", "name": "Bridget", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9117, "dossier_id": "synthesia/stock/bruno", "name": "Bruno", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9118, "dossier_id": "synthesia/stock/cadence", "name": "Cadence", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9119, "dossier_id": "synthesia/stock/callum", "name": "Callum", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9120, "dossier_id": "synthesia/stock/carla", "name": "Carla", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9121, "dossier_id": "synthesia/stock/carlo", "name": "Carlo", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9122, "dossier_id": "synthesia/stock/carly", "name": "Carly", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9123, "dossier_id": "synthesia/stock/carmine", "name": "Carmine", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9124, "dossier_id": "synthesia/stock/caroline", "name": "Caroline", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9125, "dossier_id": "synthesia/stock/carter", "name": "Carter", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9126, "dossier_id": "synthesia/stock/charles", "name": "Charles", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9127, "dossier_id": "synthesia/stock/charlotte", "name": "Charlotte", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9128, "dossier_id": "synthesia/stock/chauncy", "name": "Chauncy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9129, "dossier_id": "synthesia/stock/chef-collie", "name": "Chef Collie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9130, "dossier_id": "synthesia/stock/christina", "name": "Christina", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9131, "dossier_id": "synthesia/stock/chuck", "name": "Chuck", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9132, "dossier_id": "synthesia/stock/chun", "name": "Chun", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9133, "dossier_id": "synthesia/stock/claudia", "name": "Claudia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9134, "dossier_id": "synthesia/stock/cleo", "name": "Cleo", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9135, "dossier_id": "synthesia/stock/clive", "name": "Clive", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9136, "dossier_id": "synthesia/stock/coach-phil", "name": "Coach Phil", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9137, "dossier_id": "synthesia/stock/colette", "name": "Colette", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9138, "dossier_id": "synthesia/stock/cooper", "name": "Cooper", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9139, "dossier_id": "synthesia/stock/cyrus", "name": "Cyrus", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9140, "dossier_id": "synthesia/stock/daisy", "name": "Daisy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9141, "dossier_id": "synthesia/stock/dalel", "name": "Dalel", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9142, "dossier_id": "synthesia/stock/dammie", "name": "Dammie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9143, "dossier_id": "synthesia/stock/daphne", "name": "Daphne", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9144, "dossier_id": "synthesia/stock/daren", "name": "Daren", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9145, "dossier_id": "synthesia/stock/daria", "name": "Daria", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9146, "dossier_id": "synthesia/stock/dave", "name": "Dave", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9147, "dossier_id": "synthesia/stock/debbie", "name": "Debbie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9148, "dossier_id": "synthesia/stock/dominic", "name": "Dominic", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9149, "dossier_id": "synthesia/stock/dr-clara", "name": "Dr Clara", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9150, "dossier_id": "synthesia/stock/dr-dass", "name": "Dr Dass", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9151, "dossier_id": "synthesia/stock/dr-poppy", "name": "Dr Poppy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9152, "dossier_id": "synthesia/stock/edgar", "name": "Edgar", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9153, "dossier_id": "synthesia/stock/ella", "name": "Ella", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9154, "dossier_id": "synthesia/stock/ellie", "name": "Ellie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9155, "dossier_id": "synthesia/stock/emma", "name": "Emma", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9156, "dossier_id": "synthesia/stock/emory", "name": "Emory", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9157, "dossier_id": "synthesia/stock/eric", "name": "Eric", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9158, "dossier_id": "synthesia/stock/erica", "name": "Erica", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9159, "dossier_id": "synthesia/stock/erin", "name": "Erin", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9160, "dossier_id": "synthesia/stock/esha", "name": "Esha", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9161, "dossier_id": "synthesia/stock/ethan", "name": "Ethan", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9162, "dossier_id": "synthesia/stock/evelyn", "name": "Evelyn", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9163, "dossier_id": "synthesia/stock/evie", "name": "Evie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9164, "dossier_id": "synthesia/stock/faisal", "name": "Faisal", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9165, "dossier_id": "synthesia/stock/faiza", "name": "Faiza", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9166, "dossier_id": "synthesia/stock/fazwa", "name": "Fazwa", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9167, "dossier_id": "synthesia/stock/felix", "name": "Felix", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9168, "dossier_id": "synthesia/stock/francesca", "name": "Francesca", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9169, "dossier_id": "synthesia/stock/fredo", "name": "Fredo", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9170, "dossier_id": "synthesia/stock/freya", "name": "Freya", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9171, "dossier_id": "synthesia/stock/gary", "name": "Gary", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9172, "dossier_id": "synthesia/stock/george", "name": "George", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9173, "dossier_id": "synthesia/stock/grace", "name": "Grace", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9174, "dossier_id": "synthesia/stock/graham", "name": "Graham", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9175, "dossier_id": "synthesia/stock/greg", "name": "Greg", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9176, "dossier_id": "synthesia/stock/gwen", "name": "Gwen", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9177, "dossier_id": "synthesia/stock/haiden", "name": "Haiden", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9178, "dossier_id": "synthesia/stock/helen", "name": "Helen", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9179, "dossier_id": "synthesia/stock/helia", "name": "Helia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9180, "dossier_id": "synthesia/stock/henry", "name": "Henry", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9181, "dossier_id": "synthesia/stock/holly", "name": "Holly", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9182, "dossier_id": "synthesia/stock/hudson", "name": "Hudson", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9183, "dossier_id": "synthesia/stock/hugo", "name": "Hugo", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9184, "dossier_id": "synthesia/stock/imani", "name": "Imani", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9185, "dossier_id": "synthesia/stock/ines", "name": "Ines", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9186, "dossier_id": "synthesia/stock/iris", "name": "Iris", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9187, "dossier_id": "synthesia/stock/isabella", "name": "Isabella", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9188, "dossier_id": "synthesia/stock/jack", "name": "Jack", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9189, "dossier_id": "synthesia/stock/jackie", "name": "Jackie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9190, "dossier_id": "synthesia/stock/jade", "name": "Jade", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9191, "dossier_id": "synthesia/stock/james", "name": "James", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9192, "dossier_id": "synthesia/stock/jason", "name": "Jason", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9193, "dossier_id": "synthesia/stock/jaz", "name": "Jaz", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9194, "dossier_id": "synthesia/stock/jeanine", "name": "Jeanine", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9195, "dossier_id": "synthesia/stock/jenna", "name": "Jenna", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9196, "dossier_id": "synthesia/stock/jenny", "name": "Jenny", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9197, "dossier_id": "synthesia/stock/jeremy", "name": "Jeremy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9198, "dossier_id": "synthesia/stock/jess", "name": "Jess", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9199, "dossier_id": "synthesia/stock/jimmy", "name": "Jimmy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9200, "dossier_id": "synthesia/stock/jin", "name": "Jin", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9201, "dossier_id": "synthesia/stock/jodie", "name": "Jodie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9202, "dossier_id": "synthesia/stock/joel", "name": "Joel", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9203, "dossier_id": "synthesia/stock/jonathan", "name": "Jonathan", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9204, "dossier_id": "synthesia/stock/jonathan-beta", "name": "Jonathan (beta)", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9205, "dossier_id": "synthesia/stock/jordan", "name": "Jordan", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9206, "dossier_id": "synthesia/stock/joshua", "name": "Joshua", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9207, "dossier_id": "synthesia/stock/julia", "name": "Julia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9208, "dossier_id": "synthesia/stock/julie", "name": "Julie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9209, "dossier_id": "synthesia/stock/juliette", "name": "Juliette", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9210, "dossier_id": "synthesia/stock/kade", "name": "Kade", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9211, "dossier_id": "synthesia/stock/kaiden", "name": "Kaiden", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9212, "dossier_id": "synthesia/stock/katie", "name": "Katie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9213, "dossier_id": "synthesia/stock/kayla", "name": "Kayla", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9214, "dossier_id": "synthesia/stock/kenneth", "name": "Kenneth", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9215, "dossier_id": "synthesia/stock/kenzo", "name": "Kenzo", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9216, "dossier_id": "synthesia/stock/kevin", "name": "Kevin", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9217, "dossier_id": "synthesia/stock/kiara", "name": "Kiara", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9218, "dossier_id": "synthesia/stock/kira", "name": "Kira", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9219, "dossier_id": "synthesia/stock/kyle", "name": "Kyle", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9220, "dossier_id": "synthesia/stock/lara", "name": "Lara", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9221, "dossier_id": "synthesia/stock/laura", "name": "Laura", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9222, "dossier_id": "synthesia/stock/lauren", "name": "Lauren", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9223, "dossier_id": "synthesia/stock/leah", "name": "Leah", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9224, "dossier_id": "synthesia/stock/leonard", "name": "Leonard", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9225, "dossier_id": "synthesia/stock/liam", "name": "Liam", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9226, "dossier_id": "synthesia/stock/lily", "name": "Lily", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9227, "dossier_id": "synthesia/stock/linda", "name": "Linda", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9228, "dossier_id": "synthesia/stock/lisa", "name": "Lisa", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9229, "dossier_id": "synthesia/stock/logan", "name": "Logan", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9230, "dossier_id": "synthesia/stock/lois", "name": "Lois", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9231, "dossier_id": "synthesia/stock/lola", "name": "Lola", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9232, "dossier_id": "synthesia/stock/lorenzo", "name": "Lorenzo", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9233, "dossier_id": "synthesia/stock/luisa", "name": "Luisa", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9234, "dossier_id": "synthesia/stock/luna", "name": "Luna", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9235, "dossier_id": "synthesia/stock/lydia", "name": "Lydia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9236, "dossier_id": "synthesia/stock/maia", "name": "Maia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9237, "dossier_id": "synthesia/stock/marcus", "name": "Marcus", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9238, "dossier_id": "synthesia/stock/marie", "name": "Marie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9239, "dossier_id": "synthesia/stock/mario", "name": "Mario", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9240, "dossier_id": "synthesia/stock/mary", "name": "Mary", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9241, "dossier_id": "synthesia/stock/mason", "name": "Mason", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9242, "dossier_id": "synthesia/stock/matt", "name": "Matt", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9243, "dossier_id": "synthesia/stock/matthew", "name": "Matthew", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9244, "dossier_id": "synthesia/stock/mia", "name": "Mia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9245, "dossier_id": "synthesia/stock/michael", "name": "Michael", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9246, "dossier_id": "synthesia/stock/mike", "name": "Mike", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9247, "dossier_id": "synthesia/stock/molly", "name": "Molly", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9248, "dossier_id": "synthesia/stock/monica", "name": "Monica", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9249, "dossier_id": "synthesia/stock/ms-claus", "name": "Ms Claus", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9250, "dossier_id": "synthesia/stock/nancy", "name": "Nancy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9251, "dossier_id": "synthesia/stock/natalie", "name": "Natalie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9252, "dossier_id": "synthesia/stock/nelly", "name": "Nelly", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9253, "dossier_id": "synthesia/stock/nicole", "name": "Nicole", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9254, "dossier_id": "synthesia/stock/nina", "name": "Nina", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9255, "dossier_id": "synthesia/stock/nora", "name": "Nora", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9256, "dossier_id": "synthesia/stock/nurse-fran", "name": "Nurse Fran", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9257, "dossier_id": "synthesia/stock/officer-holly", "name": "Officer Holly", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9258, "dossier_id": "synthesia/stock/olivia", "name": "Olivia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9259, "dossier_id": "synthesia/stock/olu", "name": "Olu", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9260, "dossier_id": "synthesia/stock/otis", "name": "Otis", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9261, "dossier_id": "synthesia/stock/owen", "name": "Owen", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9262, "dossier_id": "synthesia/stock/paloma", "name": "Paloma", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9263, "dossier_id": "synthesia/stock/parker", "name": "Parker", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9264, "dossier_id": "synthesia/stock/paul", "name": "Paul", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9265, "dossier_id": "synthesia/stock/pierre", "name": "Pierre", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9266, "dossier_id": "synthesia/stock/ponn", "name": "Ponn", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9267, "dossier_id": "synthesia/stock/professor-vernon", "name": "Professor Vernon", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9268, "dossier_id": "synthesia/stock/quharrison", "name": "QuHarrison", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9269, "dossier_id": "synthesia/stock/rayan", "name": "Rayan", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9270, "dossier_id": "synthesia/stock/reema", "name": "Reema", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9271, "dossier_id": "synthesia/stock/richard", "name": "Richard", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9272, "dossier_id": "synthesia/stock/riley", "name": "Riley", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9273, "dossier_id": "synthesia/stock/ronja", "name": "Ronja", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9274, "dossier_id": "synthesia/stock/rory", "name": "Rory", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9275, "dossier_id": "synthesia/stock/rosa", "name": "Rosa", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9276, "dossier_id": "synthesia/stock/rosie", "name": "Rosie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9277, "dossier_id": "synthesia/stock/ruby", "name": "Ruby", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9278, "dossier_id": "synthesia/stock/ryan", "name": "Ryan", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9279, "dossier_id": "synthesia/stock/sally", "name": "Sally", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9280, "dossier_id": "synthesia/stock/samuel", "name": "Samuel", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9281, "dossier_id": "synthesia/stock/sana", "name": "Sana", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9282, "dossier_id": "synthesia/stock/sang", "name": "Sang", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9283, "dossier_id": "synthesia/stock/santa", "name": "Santa", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9284, "dossier_id": "synthesia/stock/sanvi", "name": "Sanvi", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9285, "dossier_id": "synthesia/stock/sia", "name": "Sia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9286, "dossier_id": "synthesia/stock/sonia", "name": "Sonia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9287, "dossier_id": "synthesia/stock/srishti", "name": "Srishti", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9288, "dossier_id": "synthesia/stock/sydonie", "name": "Sydonie", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9289, "dossier_id": "synthesia/stock/talia", "name": "Talia", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9290, "dossier_id": "synthesia/stock/tammy", "name": "Tammy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9291, "dossier_id": "synthesia/stock/theresa", "name": "Theresa", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9292, "dossier_id": "synthesia/stock/thomas", "name": "Thomas", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9293, "dossier_id": "synthesia/stock/tiffany", "name": "Tiffany", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9294, "dossier_id": "synthesia/stock/tilly", "name": "Tilly", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9295, "dossier_id": "synthesia/stock/timothy", "name": "Timothy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9296, "dossier_id": "synthesia/stock/tony", "name": "Tony", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9297, "dossier_id": "synthesia/stock/trent", "name": "Trent", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9298, "dossier_id": "synthesia/stock/tyra", "name": "Tyra", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9299, "dossier_id": "synthesia/stock/veronika", "name": "Veronika", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9300, "dossier_id": "synthesia/stock/vincent", "name": "Vincent", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9301, "dossier_id": "synthesia/stock/violet", "name": "Violet", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9302, "dossier_id": "synthesia/stock/vivienne", "name": "Vivienne", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9303, "dossier_id": "synthesia/stock/wendy", "name": "Wendy", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9304, "dossier_id": "synthesia/stock/will", "name": "Will", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9305, "dossier_id": "synthesia/stock/william", "name": "William", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9306, "dossier_id": "synthesia/stock/yasmin", "name": "Yasmin", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9307, "dossier_id": "synthesia/stock/zola", "name": "Zola", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9308, "dossier_id": "synthesia/stock/zuri", "name": "Zuri", "maker": "synthesia", "family": "synthesia/stock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9309, "dossier_id": "synthesia/syren/syren", "name": "Syren", "maker": "synthesia", "family": "synthesia/syren", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9310, "dossier_id": "systran/legacy/rbmt", "name": "SYSTRAN RBMT", "maker": "systran", "family": "systran/legacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9311, "dossier_id": "systran/legacy/rbmt-spe", "name": "SYSTRAN RBMT+SPE", "maker": "systran", "family": "systran/legacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9312, "dossier_id": "systran/legacy/smt", "name": "SYSTRAN SMT", "maker": "systran", "family": "systran/legacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9313, "dossier_id": "systran/legacy/spe", "name": "SYSTRAN SPE", "maker": "systran", "family": "systran/legacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9314, "dossier_id": "systran/pnmt/pnmt-academic", "name": "PNMT Academic", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9315, "dossier_id": "systran/pnmt/pnmt-aerospace", "name": "PNMT Aerospace", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9316, "dossier_id": "systran/pnmt/pnmt-agribusiness", "name": "PNMT Agribusiness", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9317, "dossier_id": "systran/pnmt/pnmt-agriculture", "name": "PNMT Agriculture", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9318, "dossier_id": "systran/pnmt/pnmt-air", "name": "PNMT Air", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9319, "dossier_id": "systran/pnmt/pnmt-all", "name": "PNMT All", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9320, "dossier_id": "systran/pnmt/pnmt-automotive", "name": "PNMT Automotive", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9321, "dossier_id": "systran/pnmt/pnmt-blog", "name": "PNMT Blog", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9322, "dossier_id": "systran/pnmt/pnmt-business", "name": "PNMT Business", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9323, "dossier_id": "systran/pnmt/pnmt-colloquial", "name": "PNMT Colloquial", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9324, "dossier_id": "systran/pnmt/pnmt-construction", "name": "PNMT Construction", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9325, "dossier_id": "systran/pnmt/pnmt-corona-crisis-corpus", "name": "PNMT CoronaCrisisCorpus", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9326, "dossier_id": "systran/pnmt/pnmt-culture", "name": "PNMT Culture", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9327, "dossier_id": "systran/pnmt/pnmt-customs", "name": "PNMT Customs", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9328, "dossier_id": "systran/pnmt/pnmt-dialog", "name": "PNMT Dialog", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9329, "dossier_id": "systran/pnmt/pnmt-economics", "name": "PNMT Economics", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9330, "dossier_id": "systran/pnmt/pnmt-economy", "name": "PNMT Economy", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9331, "dossier_id": "systran/pnmt/pnmt-education", "name": "PNMT Education", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9332, "dossier_id": "systran/pnmt/pnmt-electric", "name": "PNMT Electric", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9333, "dossier_id": "systran/pnmt/pnmt-electronics", "name": "PNMT Electronics", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9334, "dossier_id": "systran/pnmt/pnmt-energy", "name": "PNMT Energy", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9335, "dossier_id": "systran/pnmt/pnmt-finance", "name": "PNMT Finance", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9336, "dossier_id": "systran/pnmt/pnmt-finance-kyc", "name": "PNMT FinanceKYC", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9337, "dossier_id": "systran/pnmt/pnmt-games", "name": "PNMT Games", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9338, "dossier_id": "systran/pnmt/pnmt-general", "name": "PNMT General", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9339, "dossier_id": "systran/pnmt/pnmt-generic", "name": "PNMT Generic", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9340, "dossier_id": "systran/pnmt/pnmt-generic-cyrillic-script", "name": "PNMT Generic Cyrillic-script", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9341, "dossier_id": "systran/pnmt/pnmt-generic-formal", "name": "PNMT Generic Formal", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9342, "dossier_id": "systran/pnmt/pnmt-generic-latin-script", "name": "PNMT Generic Latin-script", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9343, "dossier_id": "systran/pnmt/pnmt-generic-loca-br", "name": "PNMT Generic Loca BR", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9344, "dossier_id": "systran/pnmt/pnmt-generic-loca-ca", "name": "PNMT Generic loca CA", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9345, "dossier_id": "systran/pnmt/pnmt-generic-loca-ch", "name": "PNMT Generic Loca CH", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9346, "dossier_id": "systran/pnmt/pnmt-generic-loca-gb", "name": "PNMT Generic Loca GB", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9347, "dossier_id": "systran/pnmt/pnmt-generic-loca-latam", "name": "PNMT Generic Loca LATAM", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9348, "dossier_id": "systran/pnmt/pnmt-generic-loca-mx", "name": "PNMT Generic Loca MX", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9349, "dossier_id": "systran/pnmt/pnmt-generic-loca-pt", "name": "PNMT Generic Loca PT", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9350, "dossier_id": "systran/pnmt/pnmt-generic-multi-scripts", "name": "PNMT Generic Multi Scripts", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9351, "dossier_id": "systran/pnmt/pnmt-generic-pinyin", "name": "PNMT Generic Pinyin", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9352, "dossier_id": "systran/pnmt/pnmt-generic-plus", "name": "PNMT GenericPlus", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9353, "dossier_id": "systran/pnmt/pnmt-generic-scl", "name": "PNMT Generic SCL", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9354, "dossier_id": "systran/pnmt/pnmt-generic-zt", "name": "PNMT Generic ZT", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9355, "dossier_id": "systran/pnmt/pnmt-guitars", "name": "PNMT Guitars", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9356, "dossier_id": "systran/pnmt/pnmt-hardware", "name": "PNMT Hardware", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9357, "dossier_id": "systran/pnmt/pnmt-health", "name": "PNMT Health", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9358, "dossier_id": "systran/pnmt/pnmt-healthcare", "name": "PNMT Healthcare", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9359, "dossier_id": "systran/pnmt/pnmt-human-sciences", "name": "PNMT HumanSciences", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9360, "dossier_id": "systran/pnmt/pnmt-instruction", "name": "PNMT Instruction", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9361, "dossier_id": "systran/pnmt/pnmt-it", "name": "PNMT IT", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9362, "dossier_id": "systran/pnmt/pnmt-it-loca-pt", "name": "PNMT IT Loca PT", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9363, "dossier_id": "systran/pnmt/pnmt-labor", "name": "PNMT Labor", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9364, "dossier_id": "systran/pnmt/pnmt-legal", "name": "PNMT Legal", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9365, "dossier_id": "systran/pnmt/pnmt-legal-frca", "name": "PNMT Legal-FRCA", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9366, "dossier_id": "systran/pnmt/pnmt-life-science", "name": "PNMT LifeScience", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9367, "dossier_id": "systran/pnmt/pnmt-life-sciences", "name": "PNMT Life-sciences", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9368, "dossier_id": "systran/pnmt/pnmt-literature", "name": "PNMT Literature", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9369, "dossier_id": "systran/pnmt/pnmt-manual", "name": "PNMT Manual", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9370, "dossier_id": "systran/pnmt/pnmt-manufacturing", "name": "PNMT Manufacturing", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9371, "dossier_id": "systran/pnmt/pnmt-marketing", "name": "PNMT Marketing", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9372, "dossier_id": "systran/pnmt/pnmt-mechanical", "name": "PNMT Mechanical", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9373, "dossier_id": "systran/pnmt/pnmt-medical", "name": "PNMT Medical", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9374, "dossier_id": "systran/pnmt/pnmt-medical-device", "name": "PNMT Medical Device", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9375, "dossier_id": "systran/pnmt/pnmt-misc", "name": "PNMT Misc", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9376, "dossier_id": "systran/pnmt/pnmt-news", "name": "PNMT News", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9377, "dossier_id": "systran/pnmt/pnmt-oil-and-gas", "name": "PNMT Oil-and-Gas", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9378, "dossier_id": "systran/pnmt/pnmt-oil-gas", "name": "PNMT OilGas", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9379, "dossier_id": "systran/pnmt/pnmt-patent", "name": "PNMT Patent", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9380, "dossier_id": "systran/pnmt/pnmt-pharma", "name": "PNMT Pharma", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9381, "dossier_id": "systran/pnmt/pnmt-public-transport", "name": "PNMT PublicTransport", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9382, "dossier_id": "systran/pnmt/pnmt-religion", "name": "PNMT Religion", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9383, "dossier_id": "systran/pnmt/pnmt-report", "name": "PNMT Report", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9384, "dossier_id": "systran/pnmt/pnmt-retail", "name": "PNMT Retail", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9385, "dossier_id": "systran/pnmt/pnmt-science-technology", "name": "PNMT ScienceTechnology", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9386, "dossier_id": "systran/pnmt/pnmt-technical", "name": "PNMT Technical", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9387, "dossier_id": "systran/pnmt/pnmt-tourism", "name": "PNMT Tourism", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9388, "dossier_id": "systran/pnmt/pnmt-weather-forecast", "name": "PNMT WeatherForecast", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9389, "dossier_id": "systran/pnmt/pnmt-web", "name": "PNMT Web", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9390, "dossier_id": "systran/pnmt/pnmt-website", "name": "PNMT Website", "maker": "systran", "family": "systran/pnmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9391, "dossier_id": "sysu/cellfm/cellfm", "name": "CellFM", "maker": "sysu", "family": "sysu/cellfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9392, "dossier_id": "sysu/ecg-lfm/ecg-lfm", "name": "ECG-LFM", "maker": "sysu", "family": "sysu/ecg-lfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9393, "dossier_id": "tampere/mad-twinnet/mad-twinnet", "name": "MaD TwinNet", "maker": "tampere", "family": "tampere/mad-twinnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9394, "dossier_id": "tamu/orbevo/orbevo-dm", "name": "OrbEvo-DM", "maker": "tamu", "family": "tamu/orbevo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9395, "dossier_id": "tamu/orbevo/orbevo-wf", "name": "OrbEvo-WF", "maker": "tamu", "family": "tamu/orbevo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9396, "dossier_id": "tamu/orbflow/orbflow", "name": "OrbFlow", "maker": "tamu", "family": "tamu/orbflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9397, "dossier_id": "tartu/estbert/estbert-ner-v2", "name": "EstBERT NER v2", "maker": "tartu", "family": "tartu/estbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9398, "dossier_id": "tartu/estllm/llama-3-1-estllm-70b", "name": "Llama-3.1-EstLLM-70B", "maker": "tartu", "family": "tartu/estllm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9399, "dossier_id": "tartu/estllm/llama-3-1-estllm-70b-instruct", "name": "Llama-3.1-EstLLM-70B-Instruct", "maker": "tartu", "family": "tartu/estllm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9400, "dossier_id": "tartu/estroberta/estroberta", "name": "EstRoBERTa", "maker": "tartu", "family": "tartu/estroberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The filled-in card fields name Estonian feature extraction. Direct-use text is still the template phrase More Information Needed.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9401, "dossier_id": "tartu/glilem/glilem", "name": "GLiLEM", "maker": "tartu", "family": "tartu/glilem", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9402, "dossier_id": "tartu/llammas/llammas", "name": "Llammas", "maker": "tartu", "family": "tartu/llammas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9403, "dossier_id": "tartu/llammas/llammas-base", "name": "Llammas-base", "maker": "tartu", "family": "tartu/llammas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9404, "dossier_id": "tartu/llammas/llammas-translate", "name": "Llammas-translate", "maker": "tartu", "family": "tartu/llammas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9405, "dossier_id": "tartu/mtee/mtee-crisis", "name": "MTee crisis", "maker": "tartu", "family": "tartu/mtee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Crisis-domain translation. The card says the domain is mostly healthcare-related. Same six directions as the other MTee checkpoints.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9406, "dossier_id": "tartu/mtee/mtee-general", "name": "MTee general", "maker": "tartu", "family": "tartu/mtee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General-domain translation among Estonian, English, German, and Russian. Built by TartuNLP and Tilde.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9407, "dossier_id": "tartu/mtee/mtee-legal", "name": "MTee legal", "maker": "tartu", "family": "tartu/mtee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Legal-domain translation among Estonian, English, German, and Russian. Same six directions as the other MTee checkpoints.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9408, "dossier_id": "tartu/mtee/mtee-military", "name": "MTee military", "maker": "tartu", "family": "tartu/mtee", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9409, "dossier_id": "tartu/neurokone/neurokone", "name": "Neurok\u00f5ne", "maker": "tartu", "family": "tartu/neurokone", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9410, "dossier_id": "tartu/septilang/septilang", "name": "Septilang", "maker": "tartu", "family": "tartu/septilang", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Many-to-many translation among Estonian, English, German, Russian, Finnish, Lithuanian, and Latvian.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9411, "dossier_id": "tartu/smugri/llama-smugri-7b", "name": "Llama-SMUGRI-7B", "maker": "tartu", "family": "tartu/smugri", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9412, "dossier_id": "tartu/smugri/llama-smugri-7b-instruct", "name": "Llama-SMUGRI-7B-Instruct", "maker": "tartu", "family": "tartu/smugri", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9413, "dossier_id": "tartu/smugri/m2m100-418m-smugri", "name": "m2m100-418M-smugri", "maker": "tartu", "family": "tartu/smugri", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Neural machine translation among the languages listed on the card, including the added codes liv, vro, sma, and sme.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9414, "dossier_id": "tartu/synest/synest", "name": "SynEst", "maker": "tartu", "family": "tartu/synest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9415, "dossier_id": "tartu/towers/tahetorn-9b", "name": "T\u00e4hetorn 9B", "maker": "tartu", "family": "tartu/towers", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9416, "dossier_id": "tartu/towers/tigutorn-2b", "name": "Tigutorn 2B", "maker": "tartu", "family": "tartu/towers", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9417, "dossier_id": "tartu/xtts/xtts-v2-est", "name": "XTTS-v2-est", "maker": "tartu", "family": "tartu/xtts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Estonian speech synthesis from the XTTS v2 fine-tune described on the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9418, "dossier_id": "tartu/xtts/xtts-v2-multi", "name": "XTTS-v2-multi", "maker": "tartu", "family": "tartu/xtts", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9419, "dossier_id": "tau/mdm/mdm", "name": "MDM", "maker": "tau", "family": "tau/mdm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9420, "dossier_id": "tau/sled/t5-v1-1-large-sled", "name": "T5-v1.1-large SLED", "maker": "tau", "family": "tau/sled", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9421, "dossier_id": "tavus/griffin/griffin", "name": "Griffin", "maker": "tavus", "family": "tavus/griffin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9422, "dossier_id": "tavus/griffin/griffin-lite", "name": "Griffin-Lite", "maker": "tavus", "family": "tavus/griffin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9423, "dossier_id": "tavus/hummingbird/hummingbird-0", "name": "Hummingbird-0", "maker": "tavus", "family": "tavus/hummingbird", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9424, "dossier_id": "tavus/phoenix/phoenix-1", "name": "Phoenix-1", "maker": "tavus", "family": "tavus/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9425, "dossier_id": "tavus/phoenix/phoenix-2", "name": "Phoenix-2", "maker": "tavus", "family": "tavus/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9426, "dossier_id": "tavus/phoenix/phoenix-3", "name": "Phoenix-3", "maker": "tavus", "family": "tavus/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9427, "dossier_id": "tavus/phoenix/phoenix-4", "name": "Phoenix-4", "maker": "tavus", "family": "tavus/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9428, "dossier_id": "tavus/phoenix/phoenix-4-5", "name": "Phoenix-4.5", "maker": "tavus", "family": "tavus/phoenix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9429, "dossier_id": "tavus/raven/raven-0", "name": "Raven-0", "maker": "tavus", "family": "tavus/raven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9430, "dossier_id": "tavus/raven/raven-1", "name": "Raven-1", "maker": "tavus", "family": "tavus/raven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9431, "dossier_id": "tavus/sparrow/sparrow-0", "name": "Sparrow-0", "maker": "tavus", "family": "tavus/sparrow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9432, "dossier_id": "tavus/sparrow/sparrow-1", "name": "Sparrow-1", "maker": "tavus", "family": "tavus/sparrow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9433, "dossier_id": "tavus/sparrow/sparrow-2", "name": "Sparrow-2", "maker": "tavus", "family": "tavus/sparrow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9434, "dossier_id": "tcd/abair/abair-tts", "name": "ABAIR synthetic voices", "maker": "tcd", "family": "tcd/abair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9435, "dossier_id": "tcd/abair/eist", "name": "ABAIR-\u00c9IST", "maker": "tcd", "family": "tcd/abair", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9436, "dossier_id": "tcd/qomhra/qomhra", "name": "Qomhr\u00e1", "maker": "tcd", "family": "tcd/qomhra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9437, "dossier_id": "technion/deep-storm/deep-storm", "name": "Deep-STORM", "maker": "technion", "family": "technion/deep-storm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9438, "dossier_id": "technion/deep-storm/deepstorm3d", "name": "DeepSTORM3D", "maker": "technion", "family": "technion/deep-storm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9439, "dossier_id": "technion/diffar/diffar", "name": "DiffAR", "maker": "technion", "family": "technion/diffar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9440, "dossier_id": "technion/sinddm/sinddm", "name": "SinDDM", "maker": "technion", "family": "technion/sinddm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9441, "dossier_id": "technion/singan/singan", "name": "SinGAN", "maker": "technion", "family": "technion/singan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9442, "dossier_id": "temple/drl-vo/drl-vo", "name": "DRL-VO", "maker": "temple", "family": "temple/drl-vo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9443, "dossier_id": "temple/s3-net/s3-net", "name": "S\u00b3-Net", "maker": "temple", "family": "temple/s3-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9444, "dossier_id": "temple/semantic-cnn/semantic-cnn", "name": "Semantic CNN", "maker": "temple", "family": "temple/semantic-cnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9445, "dossier_id": "tencent/auk/auk", "name": "AuK", "maker": "tencent", "family": "tencent/auk", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate and edit speech.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9446, "dossier_id": "tencent/hunyuan/hunyuan-0-5b", "name": "Hunyuan-0.5B", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tencent says the dense series covers devices and high-concurrency service, from a 0.5B model up to 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9447, "dossier_id": "tencent/hunyuan/hunyuan-1-8b", "name": "Hunyuan-1.8B", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tencent says the dense series covers devices and high-concurrency service, from a 0.5B model up to 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9448, "dossier_id": "tencent/hunyuan/hunyuan-2-0", "name": "Hunyuan 2.0", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9449, "dossier_id": "tencent/hunyuan/hunyuan-4b", "name": "Hunyuan-4B", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tencent says the dense series covers devices and high-concurrency service, from a 0.5B model up to 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9450, "dossier_id": "tencent/hunyuan/hunyuan-7b", "name": "Hunyuan-7B", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tencent says the dense series covers devices and high-concurrency service, from a 0.5B model up to 7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9451, "dossier_id": "tencent/hunyuan/hunyuan-7b-0124", "name": "Tencent-Hunyuan-7B-0124", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A product description beyond the repository name was not captured.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9452, "dossier_id": "tencent/hunyuan/hunyuan-code", "name": "Hunyuan-Code", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A code-oriented Hunyuan API id. A separate open code model card was not found.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9453, "dossier_id": "tencent/hunyuan/hunyuan-embedding", "name": "Hunyuan-Embedding", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": 0.7, "completion_price_per_m": 0.7, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text embeddings for the older Hunyuan API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9454, "dossier_id": "tencent/hunyuan/hunyuan-funcall", "name": "Hunyuan-FunctionCall", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A function-calling Hunyuan API id on the retirement list.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9455, "dossier_id": "tencent/hunyuan/hunyuan-lite", "name": "Hunyuan-Lite", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The smallest older Hunyuan text tier, used for high-volume simple calls.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9456, "dossier_id": "tencent/hunyuan/hunyuan-pro", "name": "Hunyuan-Pro", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9457, "dossier_id": "tencent/hunyuan/hunyuan-role", "name": "Hunyuan-Role", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": 2.4, "completion_price_per_m": 9.6, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tencent says it keeps a character consistent across turns for role play and emotional chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9458, "dossier_id": "tencent/hunyuan/hunyuan-standard", "name": "Hunyuan-Standard", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9459, "dossier_id": "tencent/hunyuan/hunyuan-t1", "name": "Hunyuan-T1", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": 4.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9460, "dossier_id": "tencent/hunyuan/hunyuan-turbo", "name": "Hunyuan-Turbo", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9461, "dossier_id": "tencent/hunyuan/hunyuan-turbos", "name": "Hunyuan-TurboS", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9462, "dossier_id": "tencent/hunyuan/hy-asr-3-0-preview", "name": "Hy-ASR-3.0-preview", "maker": "tencent", "family": "tencent/hunyuan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Speech to text with semantic understanding, including dialects.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9463, "dossier_id": "tencent/hunyuan-mt/hunyuan-translation", "name": "Hunyuan-Translation", "maker": "tencent", "family": "tencent/hunyuan-mt", "context_length": null, "prompt_price_per_m": 1.2, "completion_price_per_m": 3.6, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Machine translation across the languages Tencent listed, including Tibetan, Kazakh, Mongolian, Uyghur, and Cantonese.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9464, "dossier_id": "tencent/hunyuan-ocr/hunyuan-ocr", "name": "HunyuanOCR", "maker": "tencent", "family": "tencent/hunyuan-ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Document parsing, text spotting, information extraction, and text-image translation in one small VLM.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9465, "dossier_id": "tencent/hunyuan-vision/hunyuan-t1-vision", "name": "Hunyuan-T1-Vision", "maker": "tencent", "family": "tencent/hunyuan-vision", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 9.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image understanding on the T1 line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9466, "dossier_id": "tencent/hunyuan-vision/hunyuan-turbos-vision", "name": "Hunyuan-TurboS-Vision", "maker": "tencent", "family": "tencent/hunyuan-vision", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 9.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image understanding on the TurboS line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9467, "dossier_id": "tencent/hunyuan-vision/hunyuan-turbos-vision-video", "name": "Hunyuan-TurboS-Vision-Video", "maker": "tencent", "family": "tencent/hunyuan-vision", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 9.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video understanding on the TurboS vision line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9468, "dossier_id": "tencent/hunyuan-vision/hy-vision-1-5", "name": "Tencent HY Vision 1.5 Instruct", "maker": "tencent", "family": "tencent/hunyuan-vision", "context_length": null, "prompt_price_per_m": 3.0, "completion_price_per_m": 9.0, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The changelog says it improves basic image recognition and visual reasoning over the previous vision snapshot.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9469, "dossier_id": "tencent/hunyuan-vision/hy-vision-2-0", "name": "Hy Vision 2.0", "maker": "tencent", "family": "tencent/hunyuan-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General image-to-text understanding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9470, "dossier_id": "tencent/hunyuan3d/hunyuan3d-1", "name": "Hunyuan3D-1.0", "maker": "tencent", "family": "tencent/hunyuan3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate 3D assets from text or a single image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9471, "dossier_id": "tencent/hunyuan3d/hunyuan3d-2", "name": "Hunyuan3D-2", "maker": "tencent", "family": "tencent/hunyuan3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-resolution 3D assets from images, with separate shape and texture stages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9472, "dossier_id": "tencent/hunyuan3d/hunyuan3d-2-1", "name": "Hunyuan3D-2.1", "maker": "tencent", "family": "tencent/hunyuan3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9473, "dossier_id": "tencent/hunyuan3d/hunyuan3d-2mini", "name": "Hunyuan3D-2mini", "maker": "tencent", "family": "tencent/hunyuan3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster, smaller 3D shape generation than the 1.1B Hunyuan3D-2 shape model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9474, "dossier_id": "tencent/hunyuan3d/hunyuan3d-2mv", "name": "Hunyuan3D-2mv", "maker": "tencent", "family": "tencent/hunyuan3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate a 3D shape from several views of an object rather than one image.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9475, "dossier_id": "tencent/hunyuan3d/hunyuan3d-omni", "name": "Hunyuan3D-Omni", "maker": "tencent", "family": "tencent/hunyuan3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Controllable 3D asset generation beyond the single-image Hunyuan3D-2.1 pipeline.", "summary_intro": "Hunyuan3D-Omni is Tencent's controllable 3D generation framework. The repository was created on 2025-09-25. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9476, "dossier_id": "tencent/hunyuan3d/hunyuan3d-part", "name": "Hunyuan3D-Part", "maker": "tencent", "family": "tencent/hunyuan3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Part-aware 3D segmentation and generation for asset editing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9477, "dossier_id": "tencent/hunyuanimage/hunyuan-dit", "name": "Hunyuan-DiT", "maker": "tencent", "family": "tencent/hunyuanimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation, with emphasis on Chinese prompts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9478, "dossier_id": "tencent/hunyuanimage/hunyuanimage-2-1", "name": "HunyuanImage-2.1", "maker": "tencent", "family": "tencent/hunyuanimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "2K text-to-image generation under the Hunyuan community license.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9479, "dossier_id": "tencent/hunyuanimage/hunyuanimage-3-0", "name": "HunyuanImage-3.0", "maker": "tencent", "family": "tencent/hunyuanimage", "context_length": null, "prompt_price_per_m": 1.6, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation that can plan layout and render long text, plus Instruct image-to-image editing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9480, "dossier_id": "tencent/hunyuanimage/hunyuanimage-3-0-plus", "name": "HY Image 3.0 Plus", "maker": "tencent", "family": "tencent/hunyuanimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-quality visual creation than the first HunyuanImage-3.0 consumer experience, including multi-turn edits.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9481, "dossier_id": "tencent/hunyuanimage/hunyuanimage-3-5-preview", "name": "Hy-Image-3.5-preview", "maker": "tencent", "family": "tencent/hunyuanimage", "context_length": null, "prompt_price_per_m": 1.6, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image, reference-image generation, and multi-turn editing at 1K, 2K, and 4K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9482, "dossier_id": "tencent/hunyuanimage/hy-image-lite", "name": "HY-Image-Lite", "maker": "tencent", "family": "tencent/hunyuanimage", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The encyclopedia points it at product images, design assets, and game-scene iteration where speed matters.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9483, "dossier_id": "tencent/hunyuanvideo/hunyuan-gamecraft", "name": "Hunyuan-GameCraft", "maker": "tencent", "family": "tencent/hunyuanvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Interactive game video generation with hybrid history conditioning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9484, "dossier_id": "tencent/hunyuanvideo/hunyuancustom", "name": "HunyuanCustom", "maker": "tencent", "family": "tencent/hunyuanvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Custom video generation from image, audio, video, and text conditions.", "summary_intro": "HunyuanCustom is a named Tencent Hunyuan video model. The public repository was created on 2025-05-07. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9485, "dossier_id": "tencent/hunyuanvideo/hunyuanvideo", "name": "HunyuanVideo", "maker": "tencent", "family": "tencent/hunyuanvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9486, "dossier_id": "tencent/hunyuanvideo/hunyuanvideo-1-5", "name": "HunyuanVideo-1.5", "maker": "tencent", "family": "tencent/hunyuanvideo", "context_length": null, "prompt_price_per_m": 1.6, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Shorter, cheaper video generation than the 13B HunyuanVideo model, including 480p and 720p and a super-resolution path toward 1080p.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9487, "dossier_id": "tencent/hunyuanvideo/hunyuanvideo-avatar", "name": "HunyuanVideo-Avatar", "maker": "tencent", "family": "tencent/hunyuanvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-video avatar generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9488, "dossier_id": "tencent/hunyuanvideo/hunyuanvideo-foley", "name": "HunyuanVideo-Foley", "maker": "tencent", "family": "tencent/hunyuanvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Foley audio from video and text. The repository describes a multimodal diffusion model for sound effects.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9489, "dossier_id": "tencent/hunyuanvideo/hunyuanvideo-i2v", "name": "HunyuanVideo-I2V", "maker": "tencent", "family": "tencent/hunyuanvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Animate a reference image with a text instruction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9490, "dossier_id": "tencent/hunyuanvideo/omniweaving", "name": "OmniWeaving", "maker": "tencent", "family": "tencent/hunyuanvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Unified video generation on top of HunyuanVideo-1.5, including text, frames, editing, and reference video.", "summary_intro": "OmniWeaving is a named Tencent Hunyuan video model. The public repository was created on 2026-03-31. Collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9491, "dossier_id": "tencent/hunyuanworld/hunyuanworld-1-0", "name": "HunyuanWorld-1.0", "maker": "tencent", "family": "tencent/hunyuanworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9492, "dossier_id": "tencent/hunyuanworld/hunyuanworld-mirror", "name": "HunyuanWorld-Mirror", "maker": "tencent", "family": "tencent/hunyuanworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast universal 3D reconstruction. The repository tags it as an ICML 2026 model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9493, "dossier_id": "tencent/hunyuanworld/hunyuanworld-voyager", "name": "HunyuanWorld-Voyager", "maker": "tencent", "family": "tencent/hunyuanworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Camera-conditioned RGBD video for interactive world exploration, with real-time 3D reconstruction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9494, "dossier_id": "tencent/hunyuanworld/hy-world-1-5", "name": "HY-World 1.5", "maker": "tencent", "family": "tencent/hunyuanworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9495, "dossier_id": "tencent/hunyuanworld/hy-world-2-0", "name": "HY-World 2.0", "maker": "tencent", "family": "tencent/hunyuanworld", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-modal world model for reconstructing, generating, and simulating 3D worlds.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9496, "dossier_id": "tencent/hunyuanworld/hy-world-2-1", "name": "Hy-World 2.1", "maker": "tencent", "family": "tencent/hunyuanworld", "context_length": null, "prompt_price_per_m": 1.6, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9497, "dossier_id": "tencent/hy-embodied/hy-embodied", "name": "HY-Embodied", "maker": "tencent", "family": "tencent/hy-embodied", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Robot and agent models that combine vision, language, and action.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9498, "dossier_id": "tencent/hy-motion/hy-motion-1-0", "name": "HY-Motion-1.0", "maker": "tencent", "family": "tencent/hy-motion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Generate 3D character motion.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9499, "dossier_id": "thessaloniki/deepsing/deepsing", "name": "deepsing", "maker": "thessaloniki", "family": "thessaloniki/deepsing", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9500, "dossier_id": "tifr/disobind/disobind", "name": "Disobind", "maker": "tifr", "family": "tifr/disobind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9501, "dossier_id": "tifr/frabjous/frabjous", "name": "Frabjous", "maker": "tifr", "family": "tifr/frabjous", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9502, "dossier_id": "tifr/idpgpt/llps-minus-gpt", "name": "LLPS- GPT", "maker": "tifr", "family": "tifr/idpgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9503, "dossier_id": "tifr/idpgpt/llps-plus-gpt", "name": "LLPS+ GPT", "maker": "tifr", "family": "tifr/idpgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9504, "dossier_id": "tifr/idpgpt/pdb-star-gpt", "name": "PDB* GPT", "maker": "tifr", "family": "tifr/idpgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9505, "dossier_id": "tifr/idrbindnet/idrbindnet", "name": "IDRBindNet", "maker": "tifr", "family": "tifr/idrbindnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9506, "dossier_id": "tifr/vroom-sbi/vroom-sbi", "name": "VROOM-SBI", "maker": "tifr", "family": "tifr/vroom-sbi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9507, "dossier_id": "tii/falcon/falcon-180b", "name": "Falcon-180B", "maker": "tii", "family": "tii/falcon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9508, "dossier_id": "tii/falcon-2/falcon-2-11b-vlm", "name": "Falcon 2 11B VLM", "maker": "tii", "family": "tii/falcon-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-text model. First Falcon vision-language release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9509, "dossier_id": "tii/falcon-3/falcon3-audio", "name": "Falcon3-Audio", "maker": "tii", "family": "tii/falcon-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9510, "dossier_id": "tii/falcon-3/falcon3-vision", "name": "Falcon3 Vision", "maker": "tii", "family": "tii/falcon-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Image-to-text for the Falcon 3 line. English image inputs at launch, with more languages said to be planned.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9511, "dossier_id": "tii/falcon-asr/falcon-asr", "name": "Falcon-ASR", "maker": "tii", "family": "tii/falcon-asr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Transcribe speech, with a focus on Emirati Arabic and noisy or telephony audio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9512, "dossier_id": "tii/falcon-e/falcon-e-1b", "name": "Falcon-E 1B", "maker": "tii", "family": "tii/falcon-e", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Edge language model that runs the ternary stack on CPUs. Base, instruct, bfloat16, and prequantized weights are one release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9513, "dossier_id": "tii/falcon-e/falcon-e-3b", "name": "Falcon-E 3B", "maker": "tii", "family": "tii/falcon-e", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger edge ternary model in the Falcon-E pair.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9514, "dossier_id": "tii/falcon-emirati/falcon-emirati-7b", "name": "Falcon-Emirati 7B", "maker": "tii", "family": "tii/falcon-emirati", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Emirati dialect chat, including idioms, heritage, and poetry. Offered on Falcon Chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9515, "dossier_id": "tii/falcon-h1-tiny/falcon-h1-tiny-90m", "name": "Falcon-H1-Tiny 90M", "maker": "tii", "family": "tii/falcon-h1-tiny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General tiny hybrid model. Base, instruct, curriculum instruct, and pre-DPO checkpoints are published.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9516, "dossier_id": "tii/falcon-h1-tiny/falcon-h1-tiny-coder-90m", "name": "Falcon-H1-Tiny Coder 90M", "maker": "tii", "family": "tii/falcon-h1-tiny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Coding-specialized 90M hybrid named in the official Tiny collection.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9517, "dossier_id": "tii/falcon-h1-tiny/falcon-h1-tiny-multilingual-100m", "name": "Falcon-H1-Tiny Multilingual 100M", "maker": "tii", "family": "tii/falcon-h1-tiny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9518, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger Tiny-R reasoning model. The index links a post-GRPO checkpoint and a pre-GRPO checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9519, "dossier_id": "tii/falcon-h1-tiny/falcon-h1-tiny-r-90m", "name": "Falcon-H1-Tiny-R 90M", "maker": "tii", "family": "tii/falcon-h1-tiny", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reasoning tiny model. The Falcon index says Tiny-R was trained on reasoning data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9520, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Tool-calling 90M hybrid named in the official Tiny collection.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9521, "dossier_id": "tii/falcon-ocr/falcon-ocr", "name": "Falcon OCR", "maker": "tii", "family": "tii/falcon-ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Document OCR with high throughput at a small size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9522, "dossier_id": "tii/falcon-ocr/falcon-ocr-arabic", "name": "Falcon-OCR-Arabic", "maker": "tii", "family": "tii/falcon-ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic document OCR, including forms, receipts, invoices, and tables.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9523, "dossier_id": "tii/falcon-perception/falcon-perception", "name": "Falcon Perception", "maker": "tii", "family": "tii/falcon-perception", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open-vocabulary grounding and instance segmentation from a text query.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9524, "dossier_id": "tii/falcon-perception/falcon-perception-300m", "name": "Falcon Perception 300M", "maker": "tii", "family": "tii/falcon-perception", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller open-vocabulary detection model in the Perception line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9525, "dossier_id": "tii/noor/noor", "name": "NOOR", "maker": "tii", "family": "tii/noor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arabic generation for summarization, chat, and other language tasks. Launched as TII's largest Arabic model in 2022, before Falcon.", "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released NOOR. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9526, "dossier_id": "tii/siglino/siglino-0-6b", "name": "SigLino 0.6B", "maker": "tii", "family": "tii/siglino", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General vision encoder for retrieval, classification, and segmentation features.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9527, "dossier_id": "tii/siglino/siglino-30m", "name": "SigLino 30M", "maker": "tii", "family": "tii/siglino", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller or MoE SigLino vision encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9528, "dossier_id": "tii/siglino/siglino-70m", "name": "SigLino 70M", "maker": "tii", "family": "tii/siglino", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller or MoE SigLino vision encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9529, "dossier_id": "tii/siglino/siglino-moe-0-15-0-6b", "name": "SigLino MoE 0.15-0.6B", "maker": "tii", "family": "tii/siglino", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller or MoE SigLino vision encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9530, "dossier_id": "tii/siglino/siglino-moe-0-3-0-6b", "name": "SigLino MoE 0.3-0.6B", "maker": "tii", "family": "tii/siglino", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller or MoE SigLino vision encoder.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9531, "dossier_id": "tii/visper/visper", "name": "ViSpeR", "maker": "tii", "family": "tii/visper", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual visual and audio-visual speech recognition.", "summary_intro": "Technology Innovation Institute (TII), Abu Dhabi, released ViSpeR. Status on 2026-10-15: active. This note was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9532, "dossier_id": "together/aurora/aurora-spec-minimax-m2-1", "name": "Aurora-Spec-MiniMax-M2.1", "maker": "together", "family": "together/aurora", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Aurora-Spec-MiniMax-M2.1 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9533, "dossier_id": "together/aurora/aurora-spec-minimax-m2-5", "name": "Aurora-Spec-MiniMax-M2.5", "maker": "together", "family": "together/aurora", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Aurora-Spec-MiniMax-M2.5 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9534, "dossier_id": "together/aurora/aurora-spec-qwen3-coder-next-fp8", "name": "Aurora-Spec-Qwen3-Coder-Next-FP8", "maker": "together", "family": "together/aurora", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Aurora-Spec-Qwen3-Coder-Next-FP8 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9535, "dossier_id": "together/dragonfly/llama-3-1-8b-dragonfly-med-v2", "name": "Llama-3.1-8B-Dragonfly-Med-v2", "maker": "together", "family": "together/dragonfly", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomedical Dragonfly v2 for medical imaging research. Together continued the Llama 3.1 Dragonfly model on biomedical image instructions.", "summary_intro": "Together AI released Llama-3.1-8B-Dragonfly-Med-v2 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9536, "dossier_id": "together/dragonfly/llama-3-1-8b-dragonfly-v2", "name": "Llama-3.1-8B-Dragonfly-v2", "maker": "together", "family": "together/dragonfly", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Second general Dragonfly. Together retargeted the zoom-in vision-language recipe onto Llama 3.1 8B Instruct.", "summary_intro": "Together AI released Llama-3.1-8B-Dragonfly-v2 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9537, "dossier_id": "together/dragonfly/llama-3-8b-dragonfly-med-v1", "name": "Llama-3-8B-Dragonfly-Med-v1", "maker": "together", "family": "together/dragonfly", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomedical visual-language model for medical image understanding research. Together continued the general Dragonfly v1 tune on extra biomedical instructions.", "summary_intro": "Together AI released Llama-3-8B-Dragonfly-Med-v1 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9538, "dossier_id": "together/dragonfly/llama-3-8b-dragonfly-v1", "name": "Llama-3-8B-Dragonfly-v1", "maker": "together", "family": "together/dragonfly", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Llama-3-8B-Dragonfly-v1 (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9539, "dossier_id": "together/gpt-jt/gpt-jt-6b", "name": "GPT-JT-6B", "maker": "together", "family": "together/gpt-jt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released GPT-JT-6B (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9540, "dossier_id": "together/gpt-jt/gpt-jt-moderation-6b", "name": "GPT-JT-Moderation-6B", "maker": "together", "family": "together/gpt-jt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released GPT-JT-Moderation-6B (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9541, "dossier_id": "together/hybridagent/hybridagent-14b-agentic", "name": "HybridAgent-14B-Agentic", "maker": "together", "family": "together/hybridagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released HybridAgent-14B-Agentic (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9542, "dossier_id": "together/hybridagent/hybridagent-8b-agentic", "name": "HybridAgent-8B-Agentic", "maker": "together", "family": "together/hybridagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released HybridAgent-8B-Agentic (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9543, "dossier_id": "together/m1/m1-3b", "name": "M1-3B", "maker": "together", "family": "together/m1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released M1-3B (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9544, "dossier_id": "together/moaa/gemma-2-9b-it-moaa-dpo", "name": "Gemma-2-9B-it-MoAA-DPO", "maker": "together", "family": "together/moaa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Alignment research checkpoint. Together trained a DPO model on Gemma-2-9B-it with the Mixture of Agents Alignment pipeline.", "summary_intro": "Together AI released Gemma-2-9B-it-MoAA-DPO (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9545, "dossier_id": "together/moaa/gemma-2-9b-it-moaa-sft", "name": "Gemma-2-9B-it-MoAA-SFT", "maker": "together", "family": "together/moaa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Alignment research checkpoint. Together trained a SFT model on Gemma-2-9B-it with the Mixture of Agents Alignment pipeline.", "summary_intro": "Together AI released Gemma-2-9B-it-MoAA-SFT (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9546, "dossier_id": "together/moaa/llama-3-1-8b-instruct-moaa-dpo", "name": "Llama-3.1-8B-Instruct-MoAA-DPO", "maker": "together", "family": "together/moaa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Llama-3.1-8B-Instruct-MoAA-DPO (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9547, "dossier_id": "together/moaa/llama-3-1-8b-instruct-moaa-sft", "name": "Llama-3.1-8B-Instruct-MoAA-SFT", "maker": "together", "family": "together/moaa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Llama-3.1-8B-Instruct-MoAA-SFT (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9548, "dossier_id": "together/openchatkit/gpt-neoxt-chat-base-20b", "name": "GPT-NeoXT-Chat-Base-20B", "maker": "together", "family": "together/openchatkit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenChatKit's first chat model. Together fine-tuned EleutherAI's GPT-NeoX-20B for question answering, classification, extraction, and summarization.", "summary_intro": "Together AI released GPT-NeoXT-Chat-Base-20B (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9549, "dossier_id": "together/openchatkit/pythia-chat-base-7b", "name": "Pythia-Chat-Base-7B", "maker": "together", "family": "together/openchatkit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Pythia-Chat-Base-7B (active). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9550, "dossier_id": "together/redpajama/redpajama-2", "name": "RedPajama2", "maker": "together", "family": "together/redpajama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI announced RedPajama2 (announced). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9551, "dossier_id": "together/tev1/tev1-0-8b-experimental", "name": "Tev1-0.8B-experimental", "maker": "together", "family": "together/tev1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Together AI released Tev1-0.8B-experimental (preview). This dossier was collected on 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9552, "dossier_id": "tohoku/nanochat/nanochat-jp-v3-d8", "name": "nanochat-jp v3 d8", "maker": "tohoku", "family": "tohoku/nanochat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9553, "dossier_id": "tohoku/wiki-ent-vec/wiki-ent-vec", "name": "Wikipedia Entity Vectors", "maker": "tohoku", "family": "tohoku/wiki-ent-vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9554, "dossier_id": "tongji/civilgpt/civilgpt", "name": "CivilGPT", "maker": "tongji", "family": "tongji/civilgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9555, "dossier_id": "tongji/seisgpt/seisgpt", "name": "SeisGPT", "maker": "tongji", "family": "tongji/seisgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9556, "dossier_id": "tongji/xingzhi/xingzhi", "name": "\u884c\u4e4b", "maker": "tongji", "family": "tongji/xingzhi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9557, "dossier_id": "toronto/alexnet/alexnet", "name": "AlexNet", "maker": "toronto", "family": "toronto/alexnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9558, "dossier_id": "toronto/atomformer/atomformer-base", "name": "AtomFormer base", "maker": "toronto", "family": "toronto/atomformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9559, "dossier_id": "toronto/atomformer/atomformer-base-smp", "name": "AtomFormer base SMP", "maker": "toronto", "family": "toronto/atomformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9560, "dossier_id": "toronto/bioreason/bioreason", "name": "BioReason", "maker": "toronto", "family": "toronto/bioreason", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9561, "dossier_id": "toronto/bioreason/gogpt", "name": "GO-GPT", "maker": "toronto", "family": "toronto/bioreason", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9562, "dossier_id": "toronto/bioreason/gogpt-v2", "name": "GO-GPT v2", "maker": "toronto", "family": "toronto/bioreason", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9563, "dossier_id": "toronto/comma/comma-v0-1-1t", "name": "Comma v0.1-1T", "maker": "toronto", "family": "toronto/comma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9564, "dossier_id": "toronto/comma/comma-v0-1-2t", "name": "Comma v0.1-2T", "maker": "toronto", "family": "toronto/comma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9565, "dossier_id": "toronto/delbert/delbert-dcaf7", "name": "DELBERT DCAF7", "maker": "toronto", "family": "toronto/delbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9566, "dossier_id": "toronto/delbert/delbert-lrrk2", "name": "DELBERT LRRK2", "maker": "toronto", "family": "toronto/delbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9567, "dossier_id": "toronto/delbert/delbert-setdb1", "name": "DELBERT SETDB1", "maker": "toronto", "family": "toronto/delbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9568, "dossier_id": "toronto/delbert/delbert-wdr91", "name": "DELBERT WDR91", "maker": "toronto", "family": "toronto/delbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9569, "dossier_id": "toronto/ecg-fm/ecg-fm", "name": "ECG-FM", "maker": "toronto", "family": "toronto/ecg-fm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9570, "dossier_id": "toronto/factual-dpo/factuality-alignment-qwen2-5-14b", "name": "Factuality-Alignment-Qwen2.5-14B", "maker": "toronto", "family": "toronto/factual-dpo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9571, "dossier_id": "toronto/medsam/litemedsam", "name": "LiteMedSAM", "maker": "toronto", "family": "toronto/medsam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9572, "dossier_id": "toronto/medsam/medsam", "name": "MedSAM", "maker": "toronto", "family": "toronto/medsam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9573, "dossier_id": "toronto/medsam/medsam2", "name": "MedSAM2", "maker": "toronto", "family": "toronto/medsam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9574, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9575, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9576, "dossier_id": "toronto/newsmedia-bias/nmb-plus-bias-ner-bert", "name": "NMB-Plus bias NER BERT", "maker": "toronto", "family": "toronto/newsmedia-bias", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9577, "dossier_id": "toronto/open-pmc/open-pmc-clip", "name": "Open-PMC CLIP", "maker": "toronto", "family": "toronto/open-pmc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9578, "dossier_id": "toronto/open-pmc/pmc-18m-dab-detr", "name": "PMC-18M DAB-DETR", "maker": "toronto", "family": "toronto/open-pmc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9579, "dossier_id": "toronto/scgpt/scgpt", "name": "scGPT", "maker": "toronto", "family": "toronto/scgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9580, "dossier_id": "translated/lara/lara", "name": "Lara", "maker": "translated", "family": "translated/lara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9581, "dossier_id": "translated/lara/lara-3", "name": "Lara 3", "maker": "translated", "family": "translated/lara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9582, "dossier_id": "translated/lara/lara-batch", "name": "Lara Batch", "maker": "translated", "family": "translated/lara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9583, "dossier_id": "translated/lara/lara-flash", "name": "Lara Flash", "maker": "translated", "family": "translated/lara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9584, "dossier_id": "translated/lara/lara-prosa", "name": "Lara Prosa", "maker": "translated", "family": "translated/lara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9585, "dossier_id": "translated/lara/lara-think", "name": "Lara Think", "maker": "translated", "family": "translated/lara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9586, "dossier_id": "translated/lara/lara-v2", "name": "Lara V2", "maker": "translated", "family": "translated/lara", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9587, "dossier_id": "trento/caddy/caddy", "name": "CADDY", "maker": "trento", "family": "trento/caddy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9588, "dossier_id": "trento/fomm/first-order-motion-model", "name": "First Order Motion Model", "maker": "trento", "family": "trento/fomm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9589, "dossier_id": "trento/monkey-net/monkey-net", "name": "Monkey-Net", "maker": "trento", "family": "trento/monkey-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9590, "dossier_id": "trento/mscs/mscs", "name": "Motion-supervised Co-Part Segmentation", "maker": "trento", "family": "trento/mscs", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9591, "dossier_id": "trento/playable-environments/playable-environments", "name": "Playable Environments", "maker": "trento", "family": "trento/playable-environments", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9592, "dossier_id": "tripo/animation/animation", "name": "Tripo Animation", "maker": "tripo", "family": "tripo/animation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9593, "dossier_id": "tripo/h/h-1-3", "name": "Tripo v1.3", "maker": "tripo", "family": "tripo/h", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Early Tripo mesh generation kept for old v2 tasks. The vendor no longer presents it as a current choice.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9594, "dossier_id": "tripo/h/h-1-4", "name": "Tripo v1.4", "maker": "tripo", "family": "tripo/h", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9595, "dossier_id": "tripo/h/h-2-0", "name": "Tripo v2.0", "maker": "tripo", "family": "tripo/h", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9596, "dossier_id": "tripo/h/h-2-5", "name": "Tripo v2.5", "maker": "tripo", "family": "tripo/h", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9597, "dossier_id": "tripo/h/h-3-0", "name": "Tripo v3.0", "maker": "tripo", "family": "tripo/h", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9598, "dossier_id": "tripo/h/h-3-1", "name": "Tripo H3.1", "maker": "tripo", "family": "tripo/h", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9599, "dossier_id": "tripo/open/triposf", "name": "TripoSF", "maker": "tripo", "family": "tripo/open", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "High-resolution mesh reconstruction, including arbitrary topology and internal structures, up to 1024 cubed.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9600, "dossier_id": "tripo/open/triposg", "name": "TripoSG", "maker": "tripo", "family": "tripo/open", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9601, "dossier_id": "tripo/open/triposg-scribble", "name": "TripoSG-scribble", "maker": "tripo", "family": "tripo/open", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9602, "dossier_id": "tripo/open/triposplat", "name": "TripoSplat", "maker": "tripo", "family": "tripo/open", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Turn one image into a 3D Gaussian splat whose point count is not fixed in advance.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9603, "dossier_id": "tripo/open/triposr", "name": "TripoSR", "maker": "tripo", "family": "tripo/open", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Feed-forward mesh reconstruction from one image, aimed at draft speed rather than the later closed H and P models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9604, "dossier_id": "tripo/p/p1", "name": "Tripo P1", "maker": "tripo", "family": "tripo/p", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9605, "dossier_id": "tripo/p/p2", "name": "Tripo P2", "maker": "tripo", "family": "tripo/p", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9606, "dossier_id": "tripo/retopo/retopo-v1-0", "name": "Tripo Retopology v1.0", "maker": "tripo", "family": "tripo/retopo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reduce polycount with a basic decimator when smart topology is not required. face_limit is mandatory.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9607, "dossier_id": "tripo/retopo/retopo-v2-0", "name": "Tripo Smart Retopology v2.0", "maker": "tripo", "family": "tripo/retopo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Rebuild a high-poly mesh into a cleaner low-poly mesh, including optional quads, while keeping edges.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9608, "dossier_id": "tripo/retopo/retopo-v3-0", "name": "Tripo Retopology v3.0", "maker": "tripo", "family": "tripo/retopo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9609, "dossier_id": "tripo/rig/rig-v1-0", "name": "Tripo Rig v1.0", "maker": "tripo", "family": "tripo/rig", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Add a humanoid skeleton and skin weights, then retarget biped presets.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9610, "dossier_id": "tripo/rig/rig-v2-0", "name": "Tripo Rig v2.0", "maker": "tripo", "family": "tripo/rig", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9611, "dossier_id": "tripo/rig/rig-v2-5", "name": "Tripo Rig v2.5", "maker": "tripo", "family": "tripo/rig", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Auto-rig non-humanoid creatures and retarget the smaller universal preset set.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9612, "dossier_id": "tripo/rig/rig-v3-0", "name": "Tripo Rig v3.0", "maker": "tripo", "family": "tripo/rig", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Newer humanoid-capable rigging. The SDK says humanoid presets work and quadruped, hexapod, and octopod presets do not.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9613, "dossier_id": "tripo/segment/segment-v1", "name": "Tripo Segmentation v1", "maker": "tripo", "family": "tripo/segment", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Split a mesh into parts from geometry. It does not do the semantic labeling that v2 adds.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9614, "dossier_id": "tripo/segment/segment-v2", "name": "Tripo Segmentation v2", "maker": "tripo", "family": "tripo/segment", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9615, "dossier_id": "tripo/smart-uv/smart-uv", "name": "Tripo Smart UV", "maker": "tripo", "family": "tripo/smart-uv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Unwrap Tripo meshes or uploaded meshes up to 80,000 triangles or 40,000 quads so they can be textured.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9616, "dossier_id": "tripo/texture/texture-v2-0", "name": "Tripo Texture v2.0", "maker": "tripo", "family": "tripo/texture", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Re-texture an existing mesh. Superseded by texture v2.5 and later.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9617, "dossier_id": "tripo/texture/texture-v2-5", "name": "Tripo Texture v2.5", "maker": "tripo", "family": "tripo/texture", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Texture or re-texture a mesh when the geometry was made with Tripo v2.5, including PBR maps.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9618, "dossier_id": "tripo/texture/texture-v3-0", "name": "Tripo Texture v3.0", "maker": "tripo", "family": "tripo/texture", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "PBR texturing for current high-poly and Smart Mesh geometry when the newest v3.5 pass is not requested.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9619, "dossier_id": "tripo/texture/texture-v3-5", "name": "Tripo Texture v3.5", "maker": "tripo", "family": "tripo/texture", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9620, "dossier_id": "tripo/turbo/turbo-v1-0", "name": "Tripo Turbo v1.0", "maker": "tripo", "family": "tripo/turbo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9621, "dossier_id": "tromso/divvun/aanna", "name": "Aanna", "maker": "tromso", "family": "tromso/divvun", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9622, "dossier_id": "tromso/divvun/biret", "name": "Biret", "maker": "tromso", "family": "tromso/divvun", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9623, "dossier_id": "tromso/divvun/nihkol-abmut", "name": "Nihkol / \u00c1bmut", "maker": "tromso", "family": "tromso/divvun", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9624, "dossier_id": "tromso/divvun/sigga", "name": "Sigg\u00e1", "maker": "tromso", "family": "tromso/divvun", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9625, "dossier_id": "tsinghua/agentcpm/agentcpm-explore", "name": "AgentCPM-Explore", "maker": "tsinghua", "family": "tsinghua/agentcpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9626, "dossier_id": "tsinghua/agentcpm/agentcpm-gui", "name": "AgentCPM-GUI", "maker": "tsinghua", "family": "tsinghua/agentcpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device Android GUI agent for Chinese and English apps, with reinforcement fine-tuning for reasoning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9627, "dossier_id": "tsinghua/agentcpm/agentcpm-report", "name": "AgentCPM-Report", "maker": "tsinghua", "family": "tsinghua/agentcpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9628, "dossier_id": "tsinghua/codegeex/codegeex-13b", "name": "CodeGeeX-13B", "maker": "tsinghua", "family": "tsinghua/codegeex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code completion and generation. Superseded for new use by CodeGeeX2 and CodeGeeX4, which are cataloged under zhipu.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9629, "dossier_id": "tsinghua/cogview/cogview", "name": "CogView", "maker": "tsinghua", "family": "tsinghua/cogview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General-domain text-to-image generation. CogView2 and later CogView models are cataloged under zhipu.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9630, "dossier_id": "tsinghua/cpm/cpm-ant-10b", "name": "CPM-Ant-10B", "maker": "tsinghua", "family": "tsinghua/cpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese pretrained base model and the first CPM-Live milestone, including delta-tuning results on CUGE.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9631, "dossier_id": "tsinghua/cpm/cpm-bee-10b", "name": "CPM-Bee-10B", "maker": "tsinghua", "family": "tsinghua/cpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9632, "dossier_id": "tsinghua/cpm/cpm-bee-1b", "name": "CPM-Bee-1B", "maker": "tsinghua", "family": "tsinghua/cpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest official CPM-Bee checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9633, "dossier_id": "tsinghua/cpm/cpm-bee-2b", "name": "CPM-Bee-2B", "maker": "tsinghua", "family": "tsinghua/cpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller CPM-Bee checkpoint for limited GPUs.", "summary_intro": "CPM-Bee-2B is the compressed 2B CPM-Bee published on 2023-05-27. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9634, "dossier_id": "tsinghua/cpm/cpm-bee-5b", "name": "CPM-Bee-5B", "maker": "tsinghua", "family": "tsinghua/cpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mid-size CPM-Bee for machines that cannot hold the 10B checkpoint.", "summary_intro": "CPM-Bee-5B is the compressed 5B CPM-Bee published with the 10B release on 2023-05-27. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9635, "dossier_id": "tsinghua/embedding/minicpm-embedding-light", "name": "MiniCPM-Embedding-Light", "maker": "tsinghua", "family": "tsinghua/embedding", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A lighter MiniCPM embedding checkpoint than MiniCPM-Embedding.", "summary_intro": "MiniCPM-Embedding-Light is OpenBMB's smaller embedding model, uploaded 2025-01-17. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9636, "dossier_id": "tsinghua/glm/glm-10b", "name": "GLM-10B", "maker": "tsinghua", "family": "tsinghua/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "General English language model for understanding and generation, including the blank-infilling objective.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9637, "dossier_id": "tsinghua/glm/glm-10b-chinese", "name": "GLM-10B-Chinese", "maker": "tsinghua", "family": "tsinghua/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese blank-infilling language model at the 10B scale of the GLM paper release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9638, "dossier_id": "tsinghua/glm/glm-2b", "name": "GLM-2B", "maker": "tsinghua", "family": "tsinghua/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller English GLM from the same pretrained-model release as GLM-10B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9639, "dossier_id": "tsinghua/glm/glm-large-chinese", "name": "GLM-Large-Chinese", "maker": "tsinghua", "family": "tsinghua/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The Chinese large GLM from the original GLM model table.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9640, "dossier_id": "tsinghua/glm/glm-roberta-large", "name": "GLM-RoBERTa-Large", "maker": "tsinghua", "family": "tsinghua/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "GLM blank-infilling model initialized in the RoBERTa setting from the GLM paper release.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9641, "dossier_id": "tsinghua/imagereward/image-reward", "name": "ImageReward", "maker": "tsinghua", "family": "tsinghua/imagereward", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score generated images against a text prompt using human preference, rather than generate images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9642, "dossier_id": "tsinghua/longalign/longalign-6b-64k", "name": "LongAlign-6B-64k", "maker": "tsinghua", "family": "tsinghua/longalign", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-instruction chat. The card describes packing with loss weighting, sorted batching, and LongBench-Chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9643, "dossier_id": "tsinghua/longcite/longcite-glm4-9b", "name": "LongCite-GLM-4-9B", "maker": "tsinghua", "family": "tsinghua/longcite", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-context question answering with fine-grained citations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9644, "dossier_id": "tsinghua/longreward/longreward-glm4-9b", "name": "LongReward-GLM-4-9B-DPO", "maker": "tsinghua", "family": "tsinghua/longreward", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Long-context question answering after LongReward preference optimization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9645, "dossier_id": "tsinghua/longwriter/longwriter-glm4-9b", "name": "LongWriter-GLM-4-9B", "maker": "tsinghua", "family": "tsinghua/longwriter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9646, "dossier_id": "tsinghua/mathglm/mathglm", "name": "MathGLM-10B", "maker": "tsinghua", "family": "tsinghua/mathglm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Arithmetic and Chinese math word problems without an external calculator.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9647, "dossier_id": "tsinghua/mathglm/mathglm-vision", "name": "MathGLM-Vision", "maker": "tsinghua", "family": "tsinghua/mathglm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Vision math model. The task is inferred from the repository name because the card body was empty.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9648, "dossier_id": "tsinghua/mathglm/mathglm-vision-19b", "name": "MathGLM-Vision-19B", "maker": "tsinghua", "family": "tsinghua/mathglm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The 19B-named MathGLM vision checkpoint.", "summary_intro": "MathGLM-Vision-19B is a THUDM Hub repo uploaded 2024-12-05 under Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9649, "dossier_id": "tsinghua/minicpm/bitcpm4-1b", "name": "BitCPM4-1B", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The larger ternary MiniCPM4 checkpoint released beside BitCPM4-0.5B.", "summary_intro": "BitCPM4-1B is OpenBMB's 1B-class ternary MiniCPM4, released 2025-06-06 under Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9650, "dossier_id": "tsinghua/minicpm/minicpm4-mcp", "name": "MiniCPM4-MCP", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "MiniCPM4-MCP is an Apache-2.0 OpenBMB checkpoint released with MiniCPM4 on 2025-06-06. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9651, "dossier_id": "tsinghua/minicpm/minicpm4-survey", "name": "MiniCPM4-Survey", "maker": "tsinghua", "family": "tsinghua/minicpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9652, "dossier_id": "tsinghua/minicpm-v/minicpm-llama3-v-2-5", "name": "MiniCPM-Llama3-V 2.5", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "OpenBMB's May 2024 multimodal model, described on later cards as GPT-4V level for its size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9653, "dossier_id": "tsinghua/minicpm-v/minicpm-v", "name": "MiniCPM-V", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The first MiniCPM-V / OmniLMM-3B visual question-answering model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9654, "dossier_id": "tsinghua/minicpm-v/minicpm-v-2", "name": "MiniCPM-V 2.0", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The April 2024 MiniCPM-V generation for English and Chinese visual chat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9655, "dossier_id": "tsinghua/minicpm-v/minicpm-v-2-6", "name": "MiniCPM-V 2.6", "maker": "tsinghua", "family": "tsinghua/minicpm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-image, multi-image, and video understanding, including the maker's real-time video claim on iPad.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9656, "dossier_id": "tsinghua/msagpt/msagpt", "name": "MSAGPT", "maker": "tsinghua", "family": "tsinghua/msagpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Zero-shot and few-shot protein MSA generation to help structure prediction when natural MSAs are scarce.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9657, "dossier_id": "tsinghua/omnilmm/omnilmm-12b", "name": "OmniLMM-12B", "maker": "tsinghua", "family": "tsinghua/omnilmm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9658, "dossier_id": "tsinghua/rerank/minicpm-reranker", "name": "MiniCPM-Reranker", "maker": "tsinghua", "family": "tsinghua/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Rerank text passages using a MiniCPM-2B backbone.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9659, "dossier_id": "tsinghua/rerank/minicpm-reranker-light", "name": "MiniCPM-Reranker-Light", "maker": "tsinghua", "family": "tsinghua/rerank", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A smaller MiniCPM reranker for passage ranking.", "summary_intro": "MiniCPM-Reranker-Light is OpenBMB's MiniCPM-1B reranker, uploaded 2025-01-17. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9660, "dossier_id": "tsinghua/rlhf-v/rlaif-v-12b", "name": "RLAIF-V-12B", "maker": "tsinghua", "family": "tsinghua/rlhf-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger RLAIF-V trustworthiness model released with the 7B checkpoint.", "summary_intro": "RLAIF-V-12B is OpenBMB's larger RLAIF-V model, uploaded 2024-05-20 under Apache-2.0. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9661, "dossier_id": "tsinghua/rlhf-v/rlaif-v-7b", "name": "RLAIF-V-7B", "maker": "tsinghua", "family": "tsinghua/rlhf-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A more trustworthy LLaVA 1.5 7B, trained to reduce visual hallucinations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9662, "dossier_id": "tsinghua/rlhf-v/rlhf-v", "name": "RLHF-V", "maker": "tsinghua", "family": "tsinghua/rlhf-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reduce visual hallucination by training on human preference feedback.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9663, "dossier_id": "tsinghua/robot/minicpm-robotmanip", "name": "MiniCPM-RobotManip", "maker": "tsinghua", "family": "tsinghua/robot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device robot manipulation policy. The card calls it an on-device AI brain for robots.", "summary_intro": "MiniCPM-RobotManip is OpenBMB's Apache-2.0 manipulation model, uploaded 2026-07-18. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9664, "dossier_id": "tsinghua/robot/minicpm-robottrack", "name": "MiniCPM-RobotTrack", "maker": "tsinghua", "family": "tsinghua/robot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embodied target tracking. The card calls it a compact vision-language-action policy.", "summary_intro": "MiniCPM-RobotTrack is OpenBMB's Apache-2.0 tracking model, uploaded 2026-07-18. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9665, "dossier_id": "tsinghua/ssvae/ssvae", "name": "SSVAE", "maker": "tsinghua", "family": "tsinghua/ssvae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Video autoencoder whose latent spectrum is shaped for diffusion, not a text chatbot.", "summary_intro": "SSVAE is THUDM's MIT-licensed video VAE, uploaded 2025-12-04. The paper is arXiv 2512.05394. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9666, "dossier_id": "tsinghua/ui2code/ui2code-n", "name": "UI2Code^N", "maker": "tsinghua", "family": "tsinghua/ui2code", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "UI-to-code generation, UI editing, and UI polishing, with test-time scaling described on the card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9667, "dossier_id": "tsinghua/ultra/ultracm-13b", "name": "UltraCM-13B", "maker": "tsinghua", "family": "tsinghua/ultra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Write critiques of model responses using the UltraFeedback setup.", "summary_intro": "UltraCM-13B is OpenBMB's MIT-licensed critique model, uploaded 2023-09-22 with UltraFeedback. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9668, "dossier_id": "tsinghua/ultra/ultralm-13b", "name": "UltraLM-13B", "maker": "tsinghua", "family": "tsinghua/ultra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An early OpenBMB chat model trained on the UltraChat multi-turn data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9669, "dossier_id": "tsinghua/ultra/ultralm-65b", "name": "UltraLM-65B", "maker": "tsinghua", "family": "tsinghua/ultra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The larger UltraLM chat model trained with UltraChat.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9670, "dossier_id": "tsinghua/ultra/ultrarm-13b", "name": "UltraRM-13B", "maker": "tsinghua", "family": "tsinghua/ultra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score model responses. The UltraFeedback dataset is tagged on the card.", "summary_intro": "UltraRM-13B is OpenBMB's MIT-licensed reward model, uploaded 2023-09-22 with UltraFeedback. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9671, "dossier_id": "tsinghua/viscpm/viscpm-chat", "name": "VisCPM-Chat", "maker": "tsinghua", "family": "tsinghua/viscpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese and English visual conversation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9672, "dossier_id": "tsinghua/viscpm/viscpm-paint", "name": "VisCPM-Paint", "maker": "tsinghua", "family": "tsinghua/viscpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chinese and English text-to-image generation.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9673, "dossier_id": "tsinghua/visionreward/visionreward-image", "name": "VisionReward-Image", "maker": "tsinghua", "family": "tsinghua/visionreward", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score images with a multi-question preference rubric for aligning visual generators.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9674, "dossier_id": "tsinghua/visionreward/visionreward-video", "name": "VisionReward-Video", "maker": "tsinghua", "family": "tsinghua/visionreward", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Score generated videos against human preferences.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9675, "dossier_id": "tsinghua/visrag/visrag-ret", "name": "VisRAG-Ret", "maker": "tsinghua", "family": "tsinghua/visrag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Visual document retrieval. Training sets tagged on the card are VisRAG in-domain and synthetic retrieval data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9676, "dossier_id": "tsinghua/voxcpm/voxcpm-0-5b", "name": "VoxCPM-0.5B", "maker": "tsinghua", "family": "tsinghua/voxcpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Context-aware speech generation and voice cloning without a discrete speech tokenizer, as the card title states.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9677, "dossier_id": "tsinghua/voxcpm/voxcpm-1-5", "name": "VoxCPM1.5", "maker": "tsinghua", "family": "tsinghua/voxcpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The middle VoxCPM speech model, uploaded between VoxCPM-0.5B and VoxCPM2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9678, "dossier_id": "tsinghua/voxcpm/voxcpm2", "name": "VoxCPM2", "maker": "tsinghua", "family": "tsinghua/voxcpm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual tokenizer-free speech synthesis with voice cloning and voice design, per the model card tags and introduction.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9679, "dossier_id": "tsinghua/webvia/webvia-agent", "name": "WebVIA-Agent", "maker": "tsinghua", "family": "tsinghua/webvia", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9680, "dossier_id": "tsukuba/rayemb/rayemb-ctpelvic1k", "name": "RayEmb-CTPelvic1K", "maker": "tsukuba", "family": "tsukuba/rayemb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9681, "dossier_id": "tsukuba/sv-drr/sv-drr-1024", "name": "SV-DRR 1024", "maker": "tsukuba", "family": "tsukuba/sv-drr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9682, "dossier_id": "tsukuba/sv-drr/sv-drr-256", "name": "SV-DRR 256", "maker": "tsukuba", "family": "tsukuba/sv-drr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9683, "dossier_id": "tsukuba/sv-drr/sv-drr-512", "name": "SV-DRR 512", "maker": "tsukuba", "family": "tsukuba/sv-drr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9684, "dossier_id": "tu-berlin/cg-schnet/cg-schnet", "name": "cG-SchNet", "maker": "tu-berlin", "family": "tu-berlin/cg-schnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9685, "dossier_id": "tu-berlin/fieldschnet/field-schnet", "name": "FieldSchNet", "maker": "tu-berlin", "family": "tu-berlin/fieldschnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9686, "dossier_id": "tu-berlin/g-schnet/g-schnet", "name": "G-SchNet", "maker": "tu-berlin", "family": "tu-berlin/g-schnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9687, "dossier_id": "tu-berlin/icmil/icmil", "name": "ICMIL", "maker": "tu-berlin", "family": "tu-berlin/icmil", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9688, "dossier_id": "tu-berlin/nisqa/nisqa", "name": "NISQA", "maker": "tu-berlin", "family": "tu-berlin/nisqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9689, "dossier_id": "tu-berlin/nisqa/nisqa-tts", "name": "NISQA-TTS", "maker": "tu-berlin", "family": "tu-berlin/nisqa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9690, "dossier_id": "tu-berlin/painn/painn", "name": "PaiNN", "maker": "tu-berlin", "family": "tu-berlin/painn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9691, "dossier_id": "tu-berlin/schnet/schnet", "name": "SchNet", "maker": "tu-berlin", "family": "tu-berlin/schnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9692, "dossier_id": "tu-berlin/sgdml/sgdml", "name": "sGDML", "maker": "tu-berlin", "family": "tu-berlin/sgdml", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9693, "dossier_id": "tu-braunschweig/octopus/octopus", "name": "Octopus", "maker": "tu-braunschweig", "family": "tu-braunschweig/octopus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9694, "dossier_id": "tu-braunschweig/sgdepth/sgdepth", "name": "SGDepth", "maker": "tu-braunschweig", "family": "tu-braunschweig/sgdepth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9695, "dossier_id": "tu-braunschweig/tex2shape/tex2shape", "name": "Tex2Shape", "maker": "tu-braunschweig", "family": "tu-braunschweig/tex2shape", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9696, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-electra-base", "name": "ms-marco-electra-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9697, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-minilm-l12-v2", "name": "ms-marco-MiniLM-L12-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9698, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-minilm-l2-v2", "name": "ms-marco-MiniLM-L2-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9699, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-minilm-l4-v2", "name": "ms-marco-MiniLM-L4-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9700, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-minilm-l6-v2", "name": "ms-marco-MiniLM-L6-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9701, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-tinybert-l2", "name": "ms-marco-TinyBERT-L2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9702, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-tinybert-l2-v2", "name": "ms-marco-TinyBERT-L2-v2", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9703, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-tinybert-l4", "name": "ms-marco-TinyBERT-L4", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9704, "dossier_id": "tu-darmstadt/cross-encoder/ms-marco-tinybert-l6", "name": "ms-marco-TinyBERT-L6", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9705, "dossier_id": "tu-darmstadt/cross-encoder/nli-deberta-base", "name": "nli-deberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9706, "dossier_id": "tu-darmstadt/cross-encoder/nli-deberta-v3-base", "name": "nli-deberta-v3-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9707, "dossier_id": "tu-darmstadt/cross-encoder/nli-deberta-v3-small", "name": "nli-deberta-v3-small", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9708, "dossier_id": "tu-darmstadt/cross-encoder/nli-deberta-v3-xsmall", "name": "nli-deberta-v3-xsmall", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9709, "dossier_id": "tu-darmstadt/cross-encoder/nli-distilroberta-base", "name": "nli-distilroberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9710, "dossier_id": "tu-darmstadt/cross-encoder/nli-minilm2-l6-h768", "name": "nli-MiniLM2-L6-H768", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9711, "dossier_id": "tu-darmstadt/cross-encoder/nli-roberta-base", "name": "nli-roberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9712, "dossier_id": "tu-darmstadt/cross-encoder/qnli-distilroberta-base", "name": "qnli-distilroberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "QNLI cross-encoder based on SQuAD questions. The docs say the score is higher when a passage answers a question.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9713, "dossier_id": "tu-darmstadt/cross-encoder/qnli-electra-base", "name": "qnli-electra-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "QNLI cross-encoder based on SQuAD questions. The docs say the score is higher when a passage answers a question.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9714, "dossier_id": "tu-darmstadt/cross-encoder/quora-distilroberta-base", "name": "quora-distilroberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9715, "dossier_id": "tu-darmstadt/cross-encoder/quora-roberta-base", "name": "quora-roberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9716, "dossier_id": "tu-darmstadt/cross-encoder/quora-roberta-large", "name": "quora-roberta-large", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9717, "dossier_id": "tu-darmstadt/cross-encoder/stsb-distilroberta-base", "name": "stsb-distilroberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9718, "dossier_id": "tu-darmstadt/cross-encoder/stsb-roberta-base", "name": "stsb-roberta-base", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9719, "dossier_id": "tu-darmstadt/cross-encoder/stsb-roberta-large", "name": "stsb-roberta-large", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9720, "dossier_id": "tu-darmstadt/cross-encoder/stsb-tinybert-l4", "name": "stsb-TinyBERT-L4", "maker": "tu-darmstadt", "family": "tu-darmstadt/cross-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9721, "dossier_id": "tu-darmstadt/msmarco/msmarco-distilbert-cos-v5", "name": "msmarco-distilbert-cos-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9722, "dossier_id": "tu-darmstadt/msmarco/msmarco-minilm-l12-cos-v5", "name": "msmarco-MiniLM-L12-cos-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9723, "dossier_id": "tu-darmstadt/msmarco/msmarco-minilm-l6-cos-v5", "name": "msmarco-MiniLM-L6-cos-v5", "maker": "tu-darmstadt", "family": "tu-darmstadt/msmarco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "MS MARCO passage semantic-search model. The SBERT table says training used 500k query-answer pairs from MS MARCO passages.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9724, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-de-en-instruct", "name": "Occiglot-7B-DE-EN-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9725, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-es-en-instruct", "name": "Occiglot-7B-ES-EN-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9726, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-eu5-instruct", "name": "Occiglot-7B-EU5-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9727, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-fr-en-instruct", "name": "Occiglot-7B-FR-EN-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9728, "dossier_id": "tu-darmstadt/occiglot/occiglot-7b-it-en-instruct", "name": "Occiglot-7B-IT-EN-Instruct", "maker": "tu-darmstadt", "family": "tu-darmstadt/occiglot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9729, "dossier_id": "tu-dresden/cobra/cobra", "name": "COBRA", "maker": "tu-dresden", "family": "tu-dresden/cobra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9730, "dossier_id": "tu-dresden/mopadi/mopadi", "name": "MoPaDi", "maker": "tu-dresden", "family": "tu-dresden/mopadi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9731, "dossier_id": "tu-dresden/slideflame/slideflame", "name": "SlideFlame", "maker": "tu-dresden", "family": "tu-dresden/slideflame", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9732, "dossier_id": "tue/eomt/eomt", "name": "EoMT", "maker": "tue", "family": "tue/eomt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9733, "dossier_id": "tue/lvmt/lvmt", "name": "LVMT", "maker": "tue", "family": "tue/lvmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9734, "dossier_id": "tue/orion/orion-lite", "name": "Orion-Lite", "maker": "tue", "family": "tue/orion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9735, "dossier_id": "tue/pmt/pmt", "name": "PMT", "maker": "tue", "family": "tue/pmt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9736, "dossier_id": "tue/spectre/spectre", "name": "SPECTRE", "maker": "tue", "family": "tue/spectre", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9737, "dossier_id": "tue/videomt/videomt", "name": "VidEoMT", "maker": "tue", "family": "tue/videomt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9738, "dossier_id": "tuebingen/bagnet/bagnet-17", "name": "BagNet-17", "maker": "tuebingen", "family": "tuebingen/bagnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9739, "dossier_id": "tuebingen/bagnet/bagnet-33", "name": "BagNet-33", "maker": "tuebingen", "family": "tuebingen/bagnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9740, "dossier_id": "tuebingen/bagnet/bagnet-9", "name": "BagNet-9", "maker": "tuebingen", "family": "tuebingen/bagnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9741, "dossier_id": "tuebingen/deepgaze/deepgaze-i", "name": "DeepGaze I", "maker": "tuebingen", "family": "tuebingen/deepgaze", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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)`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9742, "dossier_id": "tuebingen/deepgaze/deepgaze-ii", "name": "DeepGaze II", "maker": "tuebingen", "family": "tuebingen/deepgaze", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9743, "dossier_id": "tuebingen/deepgaze/deepgaze-iie", "name": "DeepGaze IIE", "maker": "tuebingen", "family": "tuebingen/deepgaze", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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)`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9744, "dossier_id": "tuebingen/deepgaze/deepgaze-iii", "name": "DeepGaze III", "maker": "tuebingen", "family": "tuebingen/deepgaze", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9745, "dossier_id": "tuebingen/deepgaze/deepgaze-mr", "name": "DeepGaze MR", "maker": "tuebingen", "family": "tuebingen/deepgaze", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9746, "dossier_id": "tuebingen/deepgaze/deepgaze-msdb", "name": "DeepGaze MSDB", "maker": "tuebingen", "family": "tuebingen/deepgaze", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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)`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9747, "dossier_id": "tufts/conplex/conplex", "name": "ConPLex", "maker": "tufts", "family": "tufts/conplex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9748, "dossier_id": "tufts/d-script/d-script", "name": "D-SCRIPT", "maker": "tufts", "family": "tufts/d-script", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9749, "dossier_id": "tufts/d-script/topsy-turvy", "name": "Topsy-Turvy", "maker": "tufts", "family": "tufts/d-script", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9750, "dossier_id": "tufts/d-script/tt3d", "name": "TT3D", "maker": "tufts", "family": "tufts/d-script", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9751, "dossier_id": "tum/aerotransformer/aerotransformer", "name": "AeroTransformer", "maker": "tum", "family": "tum/aerotransformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9752, "dossier_id": "tum/pde-transformer/p3d", "name": "P3D", "maker": "tum", "family": "tum/pde-transformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9753, "dossier_id": "tum/pde-transformer/pde-transformer", "name": "PDE-Transformer", "maker": "tum", "family": "tum/pde-transformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9754, "dossier_id": "tum/piwm/piwm", "name": "PIWM", "maker": "tum", "family": "tum/piwm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9755, "dossier_id": "tum/revit/revit", "name": "ReViT", "maker": "tum", "family": "tum/revit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9756, "dossier_id": "tum/tadpole/tadpole", "name": "Tadpole", "maker": "tum", "family": "tum/tadpole", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9757, "dossier_id": "tum/treconlm/treconlm", "name": "TReconLM", "maker": "tum", "family": "tum/treconlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9758, "dossier_id": "turku/bluumi/bluumi", "name": "BLUUMI", "maker": "turku", "family": "turku/bluumi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9759, "dossier_id": "turku/finerweb/finerweb-line-quality", "name": "FinerWeb line quality classifier", "maker": "turku", "family": "turku/finerweb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9760, "dossier_id": "turku/fingpt/fingpt-13b", "name": "FinGPT 13B", "maker": "turku", "family": "turku/fingpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9761, "dossier_id": "turku/fingpt/fingpt-3b", "name": "FinGPT 3B", "maker": "turku", "family": "turku/fingpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9762, "dossier_id": "turku/fingpt/fingpt-8b", "name": "FinGPT 8B", "maker": "turku", "family": "turku/fingpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9763, "dossier_id": "turku/fingpt/fingpt-large", "name": "FinGPT Large", "maker": "turku", "family": "turku/fingpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9764, "dossier_id": "turku/fingpt/fingpt-medium", "name": "FinGPT Medium", "maker": "turku", "family": "turku/fingpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9765, "dossier_id": "turku/fingpt/fingpt-small", "name": "FinGPT Small", "maker": "turku", "family": "turku/fingpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9766, "dossier_id": "turku/fingpt/fingpt-xl", "name": "FinGPT XL", "maker": "turku", "family": "turku/fingpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9767, "dossier_id": "turku/finnish-modernbert/finnish-modernbert-base", "name": "Finnish ModernBERT Base", "maker": "turku", "family": "turku/finnish-modernbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9768, "dossier_id": "turku/finnish-modernbert/finnish-modernbert-large", "name": "Finnish ModernBERT Large", "maker": "turku", "family": "turku/finnish-modernbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9769, "dossier_id": "turku/finnish-modernbert/finnish-modernbert-tiny", "name": "Finnish ModernBERT Tiny", "maker": "turku", "family": "turku/finnish-modernbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9770, "dossier_id": "turku/parser/turku-neural-parser", "name": "Turku Neural Parser Pipeline", "maker": "turku", "family": "turku/parser", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9771, "dossier_id": "tuwien/lidarscout/lidarscout", "name": "LidarScout", "maker": "tuwien", "family": "tuwien/lidarscout", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9772, "dossier_id": "tuwien/model-vienna/model-vienna", "name": "Model_Vienna", "maker": "tuwien", "family": "tuwien/model-vienna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9773, "dossier_id": "tuwien/model-vienna/model-vienna-xyz", "name": "Model_Vienna_xyz", "maker": "tuwien", "family": "tuwien/model-vienna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9774, "dossier_id": "tuwien/points2surf/points2surf", "name": "Points2Surf", "maker": "tuwien", "family": "tuwien/points2surf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9775, "dossier_id": "tuwien/ppsurf/ppsurf", "name": "PPSurf", "maker": "tuwien", "family": "tuwien/ppsurf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9776, "dossier_id": "twelve-labs/marengo/marengo-2-5", "name": "Marengo 2.5", "maker": "twelve-labs", "family": "twelve-labs/marengo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Marengo 2.5 by Twelve Labs. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9777, "dossier_id": "twelve-labs/marengo/marengo-2-6", "name": "Marengo 2.6", "maker": "twelve-labs", "family": "twelve-labs/marengo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Marengo 2.6 by Twelve Labs. Status: deprecated. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9778, "dossier_id": "twelve-labs/marengo/marengo-2-7", "name": "Marengo 2.7", "maker": "twelve-labs", "family": "twelve-labs/marengo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Marengo 2.7 by Twelve Labs. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9779, "dossier_id": "twelve-labs/marengo/marengo-3-0", "name": "Marengo 3.0", "maker": "twelve-labs", "family": "twelve-labs/marengo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Marengo 3.0 by Twelve Labs. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9780, "dossier_id": "twelve-labs/marengo/marengo-3-5", "name": "Marengo 3.5", "maker": "twelve-labs", "family": "twelve-labs/marengo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Marengo 3.5 by Twelve Labs. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9781, "dossier_id": "twelve-labs/pegasus/pegasus-1-0", "name": "Pegasus 1.0", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Pegasus 1.0 by Twelve Labs. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9782, "dossier_id": "twelve-labs/pegasus/pegasus-1-2", "name": "Pegasus 1.2", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Pegasus 1.2 by Twelve Labs. Status: deprecated. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9783, "dossier_id": "twelve-labs/pegasus/pegasus-alpha", "name": "Pegasus alpha", "maker": "twelve-labs", "family": "twelve-labs/pegasus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "Pegasus alpha by Twelve Labs. Status: retired. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9784, "dossier_id": "u-tokyo/asagi/asagi-14b", "name": "Asagi-14B", "maker": "u-tokyo", "family": "u-tokyo/asagi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9785, "dossier_id": "u-tokyo/asagi/asagi-2b", "name": "Asagi-2B", "maker": "u-tokyo", "family": "u-tokyo/asagi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9786, "dossier_id": "u-tokyo/asagi/asagi-4b", "name": "Asagi-4B", "maker": "u-tokyo", "family": "u-tokyo/asagi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9787, "dossier_id": "u-tokyo/asagi/asagi-8b", "name": "Asagi-8B", "maker": "u-tokyo", "family": "u-tokyo/asagi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9788, "dossier_id": "u-tokyo/asclelm/asclelm-1-10b", "name": "AscleLM-1-10B", "maker": "u-tokyo", "family": "u-tokyo/asclelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9789, "dossier_id": "u-tokyo/med-asagi/med-asagi-14b", "name": "Med-Asagi-14B", "maker": "u-tokyo", "family": "u-tokyo/med-asagi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9790, "dossier_id": "u-tokyo/stormy/stormy-7b", "name": "Stormy-7B", "maker": "u-tokyo", "family": "u-tokyo/stormy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9791, "dossier_id": "u-tokyo/tanuki/tanuki-8b-vision", "name": "Tanuki-8B-vision", "maker": "u-tokyo", "family": "u-tokyo/tanuki", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9792, "dossier_id": "u-tokyo/tanuki/tanuki-8x8b-vision-exp", "name": "Tanuki-8x8B-vision-exp", "maker": "u-tokyo", "family": "u-tokyo/tanuki", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9793, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9794, "dossier_id": "uabirmingham/proteinrediff/proteinrediff", "name": "ProteinReDiff", "maker": "uabirmingham", "family": "uabirmingham/proteinrediff", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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 `*`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9795, "dossier_id": "ualberta/action2motion/action2motion", "name": "Action2Motion", "maker": "ualberta", "family": "ualberta/action2motion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9796, "dossier_id": "ualberta/momask/momask", "name": "MoMask", "maker": "ualberta", "family": "ualberta/momask", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9797, "dossier_id": "ualberta/text2motion/text2motion", "name": "Text2Motion", "maker": "ualberta", "family": "ualberta/text2motion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9798, "dossier_id": "ualberta/tm2t/tm2t", "name": "TM2T", "maker": "ualberta", "family": "ualberta/tm2t", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9799, "dossier_id": "uark/bubbleid/bubbleid", "name": "BubbleID", "maker": "uark", "family": "uark/bubbleid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9800, "dossier_id": "uark/bubbleid/bubbleid-flow", "name": "BubbleID-Flow", "maker": "uark", "family": "uark/bubbleid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9801, "dossier_id": "uark/micron-bert/micron-bert", "name": "Micron-BERT", "maker": "uark", "family": "uark/micron-bert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9802, "dossier_id": "uark/podnn/podnn-cold-plate", "name": "POD-NN Cold Plate", "maker": "uark", "family": "uark/podnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9803, "dossier_id": "uark/quantumflake/flake-classifier", "name": "flake-classifier", "maker": "uark", "family": "uark/quantumflake", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9804, "dossier_id": "uark/quantumflake/uark-detector-v3", "name": "uark_detector_v3", "maker": "uark", "family": "uark/quantumflake", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9805, "dossier_id": "uark/seqreg/hit2flux", "name": "Hit2Flux", "maker": "uark", "family": "uark/seqreg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9806, "dossier_id": "uark/seqreg/hydreg", "name": "HydReg", "maker": "uark", "family": "uark/seqreg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9807, "dossier_id": "uark/seqreg/imgreg", "name": "ImgReg", "maker": "uark", "family": "uark/seqreg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9808, "dossier_id": "ubc/cheetah/cheetah-1-2b", "name": "Cheetah-1.2B", "maker": "ubc", "family": "ubc/cheetah", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9809, "dossier_id": "ubc/cheetah/cheetah-3-7b", "name": "Cheetah-3.7B", "maker": "ubc", "family": "ubc/cheetah", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9810, "dossier_id": "ubc/indt5/indt5", "name": "IndT5", "maker": "ubc", "family": "ubc/indt5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9811, "dossier_id": "ubc/toucan/toucan-1-2b", "name": "Toucan-1.2B", "maker": "ubc", "family": "ubc/toucan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9812, "dossier_id": "ubc/toucan/toucan-3-7b", "name": "Toucan-3.7B", "maker": "ubc", "family": "ubc/toucan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9813, "dossier_id": "ubc/toucan/toucan-base", "name": "Toucan-base", "maker": "ubc", "family": "ubc/toucan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9814, "dossier_id": "ucc/uccix/uccix-llama2-13b-instruct", "name": "UCCIX-Llama2-13B-Instruct", "maker": "ucc", "family": "ucc/uccix", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9815, "dossier_id": "ucd/vm-management/drl-vm-management-agent", "name": "DRL VM Management Agent", "maker": "ucd", "family": "ucd/vm-management", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9816, "dossier_id": "ucdavis/cropgan/cropgan", "name": "CropGAN", "maker": "ucdavis", "family": "ucdavis/cropgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9817, "dossier_id": "ucf/geoclip/geoclip", "name": "GeoCLIP", "maker": "ucf", "family": "ucf/geoclip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9818, "dossier_id": "uchicago/da-wand/da-wand", "name": "DA Wand", "maker": "uchicago", "family": "uchicago/da-wand", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9819, "dossier_id": "uchicago/geocode/geocode", "name": "GeoCode", "maker": "uchicago", "family": "uchicago/geocode", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9820, "dossier_id": "uchicago/hyperfields/hyperfields", "name": "HyperFields", "maker": "uchicago", "family": "uchicago/hyperfields", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9821, "dossier_id": "uchicago/loopdraw/loopdraw", "name": "LoopDraw", "maker": "uchicago", "family": "uchicago/loopdraw", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9822, "dossier_id": "uchicago/meshfm/meshfm", "name": "MeshFM", "maker": "uchicago", "family": "uchicago/meshfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9823, "dossier_id": "uci/danq/danq", "name": "DanQ", "maker": "uci", "family": "uci/danq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9824, "dossier_id": "uci/danq/danq-jaspar", "name": "DanQ-JASPAR", "maker": "uci", "family": "uci/danq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9825, "dossier_id": "uci/factornet/factornet", "name": "FactorNet", "maker": "uci", "family": "uci/factornet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9826, "dossier_id": "uci/scfan/scfan", "name": "scFAN", "maker": "uci", "family": "uci/scfan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9827, "dossier_id": "uci/scratch/3dpro", "name": "3Dpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predicts a C-alpha tertiary structure from sequence. The form says 3Dpro has not been retrained since 2005.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9828, "dossier_id": "uci/scratch/abtmpro", "name": "ABTMpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9829, "dossier_id": "uci/scratch/accpro", "name": "ACCpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classifies each residue as buried or exposed at the 25 percent relative solvent-accessibility threshold.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9830, "dossier_id": "uci/scratch/accpro20", "name": "ACCpro20", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predicts relative solvent accessibility at every 5 percent threshold from 0 percent through 95 percent.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9831, "dossier_id": "uci/scratch/antigenpro", "name": "ANTIGENpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9832, "dossier_id": "uci/scratch/bepro", "name": "BEpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9833, "dossier_id": "uci/scratch/betapro", "name": "BETApro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9834, "dossier_id": "uci/scratch/cmappro", "name": "CMAPpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predicts residue-residue contact maps. The form says CMAPpro has not been retrained since 2012.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9835, "dossier_id": "uci/scratch/cobepro", "name": "COBEpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Scores continuous B-cell epitope propensity for fragments and for residues inside an antigen. The form still lists COBEpro.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9836, "dossier_id": "uci/scratch/conpro", "name": "CONpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9837, "dossier_id": "uci/scratch/dipro", "name": "DIpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9838, "dossier_id": "uci/scratch/dispro", "name": "DISpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9839, "dossier_id": "uci/scratch/dompro", "name": "DOMpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9840, "dossier_id": "uci/scratch/foldpro", "name": "FOLDpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9841, "dossier_id": "uci/scratch/mupro", "name": "MUpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9842, "dossier_id": "uci/scratch/selectpro", "name": "SELECTpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9843, "dossier_id": "uci/scratch/sidepro", "name": "SIDEpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predicts side-chain conformations for a protein backbone, including non-standard amino acids and post-translational modifications.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9844, "dossier_id": "uci/scratch/solpro", "name": "SOLpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9845, "dossier_id": "uci/scratch/sspro", "name": "SSpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9846, "dossier_id": "uci/scratch/sspro8", "name": "SSpro8", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Predicts DSSP eight-class secondary structure (H, G, I, E, B, T, S, C) from a protein sequence.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9847, "dossier_id": "uci/scratch/svmcon", "name": "SVMcon", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9848, "dossier_id": "uci/scratch/tmbpro", "name": "TMBpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9849, "dossier_id": "uci/scratch/viralpro", "name": "VIRALpro", "maker": "uci", "family": "uci/scratch", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9850, "dossier_id": "uci/ufold/ufold", "name": "UFold", "maker": "uci", "family": "uci/ufold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9851, "dossier_id": "ucl/chestfound/chestfound-dinov3", "name": "ChestFound-DINOv3", "maker": "ucl", "family": "ucl/chestfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9852, "dossier_id": "ucl/retfound/retfound-dinov2-meh", "name": "RETFound-DINOv2 Moorfields", "maker": "ucl", "family": "ucl/retfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9853, "dossier_id": "ucl/retfound/retfound-dinov2-shanghai", "name": "RETFound-DINOv2 Shanghai", "maker": "ucl", "family": "ucl/retfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9854, "dossier_id": "ucl/retfound/retfound-dinov3", "name": "RETFound-DINOv3", "maker": "ucl", "family": "ucl/retfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9855, "dossier_id": "ucl/retfound/retfound-mae-cfp", "name": "RETFound-MAE colour fundus", "maker": "ucl", "family": "ucl/retfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9856, "dossier_id": "ucl/retfound/retfound-mae-meh", "name": "RETFound-MAE Moorfields", "maker": "ucl", "family": "ucl/retfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9857, "dossier_id": "ucl/retfound/retfound-mae-oct", "name": "RETFound-MAE OCT", "maker": "ucl", "family": "ucl/retfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9858, "dossier_id": "ucl/retfound/retfound-mae-shanghai", "name": "RETFound-MAE Shanghai", "maker": "ucl", "family": "ucl/retfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9859, "dossier_id": "ucl/tubenet/tubenet", "name": "tUbeNet", "maker": "ucl", "family": "ucl/tubenet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9860, "dossier_id": "ucla/brief/brief-pro", "name": "BRIEF-Pro", "maker": "ucla", "family": "ucla/brief", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the brief family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9861, "dossier_id": "ucla/opensla/opensla-h", "name": "OpenSLA-H clinical", "maker": "ucla", "family": "ucla/opensla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the opensla family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9862, "dossier_id": "ucla/openvlthinker/openvlthinker-7b", "name": "OpenVLThinker-7B", "maker": "ucla", "family": "ucla/openvlthinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the openvlthinker family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9863, "dossier_id": "ucla/openvlthinker/openvlthinker-v2", "name": "OpenVLThinkerV2", "maker": "ucla", "family": "ucla/openvlthinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the openvlthinker family, collected on 2026-10-15. Status is announced. Weights field: not stated.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9864, "dossier_id": "ucla/osf/osf-base", "name": "OSF-Base", "maker": "ucla", "family": "ucla/osf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the osf family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9865, "dossier_id": "ucla/sensorgen/sensorgen-bp", "name": "SensorGen blood-pressure translation", "maker": "ucla", "family": "ucla/sensorgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the sensorgen family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9866, "dossier_id": "ucla/sensorgen/sensorgen-text2ecg", "name": "SensorGen text-to-ECG", "maker": "ucla", "family": "ucla/sensorgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the sensorgen family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9867, "dossier_id": "ucla/sleeplm/sleeplm-base", "name": "SleepLM-Base", "maker": "ucla", "family": "ucla/sleeplm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the sleeplm family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9868, "dossier_id": "ucla/spin/spin-diffusion", "name": "SPIN-Diffusion iteration 3", "maker": "ucla", "family": "ucla/spin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the spin family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9869, "dossier_id": "ucla/spin/zephyr-7b-spin", "name": "Zephyr-7B SPIN iteration 3", "maker": "ucla", "family": "ucla/spin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the spin family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9870, "dossier_id": "ucla/sppo/gemma-2-9b-it-sppo", "name": "Gemma-2-9B-It SPPO iteration 3", "maker": "ucla", "family": "ucla/sppo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the sppo family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9871, "dossier_id": "ucla/sppo/llama-3-8b-sppo", "name": "Llama-3-8B-Instruct SPPO iteration 3", "maker": "ucla", "family": "ucla/sppo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the sppo family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9872, "dossier_id": "ucla/sppo/mistral-7b-pairrm-sppo", "name": "Mistral-7B PairRM SPPO iteration 3", "maker": "ucla", "family": "ucla/sppo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of California, Los Angeles release in the sppo family, collected on 2026-10-15. Status is active. Weights field: open-weights.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9873, "dossier_id": "uclouvain/2-net/2-net", "name": "2-Net", "maker": "uclouvain", "family": "uclouvain/2-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9874, "dossier_id": "uclouvain/digita/digita", "name": "Digita", "maker": "uclouvain", "family": "uclouvain/digita", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9875, "dossier_id": "ucmerced/adaptsegnet/adaptsegnet", "name": "AdaptSegNet", "maker": "ucmerced", "family": "ucmerced/adaptsegnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9876, "dossier_id": "ucmerced/advsemiseg/advsemiseg", "name": "AdvSemiSeg", "maker": "ucmerced", "family": "ucmerced/advsemiseg", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9877, "dossier_id": "ucmerced/drit/drit", "name": "DRIT", "maker": "ucmerced", "family": "ucmerced/drit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9878, "dossier_id": "ucmerced/lapsrn/lapsrn", "name": "LapSRN", "maker": "ucmerced", "family": "ucmerced/lapsrn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9879, "dossier_id": "ucmerced/lapsrn/ms-lapsrn", "name": "MS-LapSRN", "maker": "ucmerced", "family": "ucmerced/lapsrn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9880, "dossier_id": "ucmerced/msgan/msgan", "name": "MSGAN", "maker": "ucmerced", "family": "ucmerced/msgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9881, "dossier_id": "uconn/timerouter/timerouter-v1", "name": "TimeRouter", "maker": "uconn", "family": "uconn/timerouter", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9882, "dossier_id": "ucph/amaes/amaes-resenc-b-fomo260k", "name": "AMAES ResEnc B FOMO260K", "maker": "ucph", "family": "ucph/amaes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9883, "dossier_id": "ucph/amaes/amaes-resenc-b-fomo300k", "name": "AMAES ResEnc B FOMO300K", "maker": "ucph", "family": "ucph/amaes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9884, "dossier_id": "ucph/cultrace/cultrace-latentqa-decoder-gemma-3-4b-it", "name": "CulTrace LatentQA decoder Gemma 3 4B Instruct", "maker": "ucph", "family": "ucph/cultrace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9885, "dossier_id": "ucph/cultrace/cultrace-latentqa-decoder-llama-3-8b-instruct", "name": "CulTrace LatentQA decoder Llama 3 8B Instruct", "maker": "ucph", "family": "ucph/cultrace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9886, "dossier_id": "ucph/cultrace/cultrace-latentqa-decoder-ministral-8b-instruct", "name": "CulTrace LatentQA decoder Ministral 8B Instruct", "maker": "ucph", "family": "ucph/cultrace", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9887, "dossier_id": "ucph/danish-legal/danish-legal-longformer-base", "name": "Danish Legal Longformer base", "maker": "ucph", "family": "ucph/danish-legal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It is the masked-language checkpoint behind the Danish EURLEX classifiers. The card itself is a Trainer MLM card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9888, "dossier_id": "ucph/danish-legal/danish-legal-longformer-eurlex", "name": "Danish Legal Longformer EURLEX", "maker": "ucph", "family": "ucph/danish-legal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9889, "dossier_id": "ucph/danish-legal/danish-legal-longformer-eurlex-sd", "name": "Danish Legal Longformer EURLEX SD", "maker": "ucph", "family": "ucph/danish-legal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9890, "dossier_id": "ucph/danish-legal/danish-legal-xlm-base", "name": "Danish Legal XLM base", "maker": "ucph", "family": "ucph/danish-legal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The GitHub table groups it with the other Danish legal language models. The card does not state an intended use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9891, "dossier_id": "ucph/exaggeration/scientific-exaggeration-detection", "name": "Scientific Exaggeration Detector", "maker": "ucph", "family": "ucph/exaggeration", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says to measure causal claim strength of a scientific sentence and to compare two sentences for exaggeration.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9892, "dossier_id": "ucph/fairlex/fairlex-cail-minilm", "name": "FairLex CAIL MiniLM", "maker": "ucph", "family": "ucph/fairlex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9893, "dossier_id": "ucph/fairlex/fairlex-ecthr-minilm", "name": "FairLex ECtHR MiniLM", "maker": "ucph", "family": "ucph/fairlex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9894, "dossier_id": "ucph/fairlex/fairlex-fscs-minilm", "name": "FairLex FSCS MiniLM", "maker": "ucph", "family": "ucph/fairlex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9895, "dossier_id": "ucph/fairlex/fairlex-scotus-minilm", "name": "FairLex SCOTUS MiniLM", "maker": "ucph", "family": "ucph/fairlex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9896, "dossier_id": "ucph/lexlm/legal-longformer-base", "name": "Legal Longformer base", "maker": "ucph", "family": "ucph/lexlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9897, "dossier_id": "ucph/lexlm/legal-longformer-large", "name": "Legal Longformer large", "maker": "ucph", "family": "ucph/lexlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9898, "dossier_id": "ucph/lexlm/legal-roberta-base", "name": "LexLM base", "maker": "ucph", "family": "ucph/lexlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9899, "dossier_id": "ucph/lexlm/legal-roberta-large", "name": "LexLM large", "maker": "ucph", "family": "ucph/lexlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9900, "dossier_id": "ucph/populism/roberta-large-ft-trump-populism", "name": "RoBERTa large Trump populism", "maker": "ucph", "family": "ucph/populism", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card limits the intended use to academic research on fine-grained populism.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9901, "dossier_id": "ucr/abflownet/abflownet", "name": "AbFlowNet", "maker": "ucr", "family": "ucr/abflownet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9902, "dossier_id": "ucr/birna/birna-bert", "name": "BiRNA-BERT", "maker": "ucr", "family": "ucr/birna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9903, "dossier_id": "ucsc/advxl/advxl-vit-g-14", "name": "AdvXL ViT-g/14", "maker": "ucsc", "family": "ucsc/advxl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "AdvXL is their large-scale adversarial-training recipe. The released models are robust visual representations, not a chat API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9904, "dossier_id": "ucsc/advxl/advxl-vit-h-14", "name": "AdvXL ViT-H/14", "maker": "ucsc", "family": "ucsc/advxl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "AdvXL is their large-scale adversarial-training recipe. The released models are robust visual representations, not a chat API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9905, "dossier_id": "ucsc/clipa/clipa-vit-bigg-14-336-datacomp1b", "name": "CLIPA ViT-bigG/14 336 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9906, "dossier_id": "ucsc/clipa/clipa-vit-bigg-14-datacomp1b", "name": "CLIPA ViT-bigG/14 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9907, "dossier_id": "ucsc/clipa/clipa-vit-h-14-336-datacomp1b", "name": "CLIPA ViT-H/14 336 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9908, "dossier_id": "ucsc/clipa/clipa-vit-h-14-336-laion2b", "name": "CLIPA ViT-H/14 336 LAION-2B", "maker": "ucsc", "family": "ucsc/clipa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9909, "dossier_id": "ucsc/clipa/clipa-vit-h-14-datacomp1b", "name": "CLIPA ViT-H/14 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9910, "dossier_id": "ucsc/clipa/clipa-vit-l-14-336-datacomp1b", "name": "CLIPA ViT-L/14 336 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9911, "dossier_id": "ucsc/clipa/clipa-vit-l-14-datacomp1b", "name": "CLIPA ViT-L/14 DataComp-1B", "maker": "ucsc", "family": "ucsc/clipa", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9912, "dossier_id": "ucsc/clips/clips-vit-h-14-224", "name": "CLIPS ViT-H/14 224", "maker": "ucsc", "family": "ucsc/clips", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9913, "dossier_id": "ucsc/clips/clips-vit-l-14-224", "name": "CLIPS ViT-L/14 224", "maker": "ucsc", "family": "ucsc/clips", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9914, "dossier_id": "ucsc/clips/clips-vit-l-14-336", "name": "CLIPS ViT-L/14 336", "maker": "ucsc", "family": "ucsc/clips", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9915, "dossier_id": "ucsc/crate/crate-alpha-b-16", "name": "CRATE-alpha B/16", "maker": "ucsc", "family": "ucsc/crate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9916, "dossier_id": "ucsc/crate/crate-alpha-clipa-h-14", "name": "CRATE-alpha CLIPA H/14", "maker": "ucsc", "family": "ucsc/crate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9917, "dossier_id": "ucsc/crate/crate-alpha-clipa-l-14", "name": "CRATE-alpha CLIPA L/14", "maker": "ucsc", "family": "ucsc/crate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9918, "dossier_id": "ucsc/crate/crate-alpha-l-14", "name": "CRATE-alpha L/14", "maker": "ucsc", "family": "ucsc/crate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9919, "dossier_id": "ucsc/digpt/digpt-vit-b", "name": "D-iGPT ViT-B", "maker": "ucsc", "family": "ucsc/digpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9920, "dossier_id": "ucsc/digpt/digpt-vit-h", "name": "D-iGPT ViT-H", "maker": "ucsc", "family": "ucsc/digpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9921, "dossier_id": "ucsc/digpt/digpt-vit-l", "name": "D-iGPT ViT-L", "maker": "ucsc", "family": "ucsc/digpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9922, "dossier_id": "ucsc/epifoundation/epifoundation", "name": "EpiFoundation", "maker": "ucsc", "family": "ucsc/epifoundation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9923, "dossier_id": "ucsc/mmfreelm/mmfreelm-1-3b", "name": "MatMul-free LM 1.3B", "maker": "ucsc", "family": "ucsc/mmfreelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9924, "dossier_id": "ucsc/mmfreelm/mmfreelm-2-7b", "name": "MatMul-free LM 2.7B", "maker": "ucsc", "family": "ucsc/mmfreelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9925, "dossier_id": "ucsc/mmfreelm/mmfreelm-370m", "name": "MatMul-free LM 370M", "maker": "ucsc", "family": "ucsc/mmfreelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9926, "dossier_id": "ucsc/openvision/openvision-vit-base-patch16-160", "name": "OpenVision ViT-Base/16 160", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9927, "dossier_id": "ucsc/openvision/openvision-vit-base-patch16-224", "name": "OpenVision ViT-Base/16 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9928, "dossier_id": "ucsc/openvision/openvision-vit-base-patch16-384", "name": "OpenVision ViT-Base/16 384", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9929, "dossier_id": "ucsc/openvision/openvision-vit-base-patch8-160", "name": "OpenVision ViT-Base/8 160", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9930, "dossier_id": "ucsc/openvision/openvision-vit-base-patch8-224", "name": "OpenVision ViT-Base/8 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9931, "dossier_id": "ucsc/openvision/openvision-vit-base-patch8-384", "name": "OpenVision ViT-Base/8 384", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9932, "dossier_id": "ucsc/openvision/openvision-vit-huge-patch14-224", "name": "OpenVision ViT-Huge/14 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9933, "dossier_id": "ucsc/openvision/openvision-vit-huge-patch14-84", "name": "OpenVision ViT-Huge/14 84", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9934, "dossier_id": "ucsc/openvision/openvision-vit-large-patch14-224", "name": "OpenVision ViT-Large/14 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9935, "dossier_id": "ucsc/openvision/openvision-vit-large-patch14-336", "name": "OpenVision ViT-Large/14 336", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9936, "dossier_id": "ucsc/openvision/openvision-vit-large-patch14-84", "name": "OpenVision ViT-Large/14 84", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9937, "dossier_id": "ucsc/openvision/openvision-vit-small-patch16-160", "name": "OpenVision ViT-Small/16 160", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9938, "dossier_id": "ucsc/openvision/openvision-vit-small-patch16-224", "name": "OpenVision ViT-Small/16 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9939, "dossier_id": "ucsc/openvision/openvision-vit-small-patch16-384", "name": "OpenVision ViT-Small/16 384", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9940, "dossier_id": "ucsc/openvision/openvision-vit-small-patch8-160", "name": "OpenVision ViT-Small/8 160", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9941, "dossier_id": "ucsc/openvision/openvision-vit-small-patch8-224", "name": "OpenVision ViT-Small/8 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9942, "dossier_id": "ucsc/openvision/openvision-vit-small-patch8-384", "name": "OpenVision ViT-Small/8 384", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9943, "dossier_id": "ucsc/openvision/openvision-vit-so400m-patch14-224", "name": "OpenVision ViT-SoViT-400M/14 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9944, "dossier_id": "ucsc/openvision/openvision-vit-so400m-patch14-384", "name": "OpenVision ViT-SoViT-400M/14 384", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9945, "dossier_id": "ucsc/openvision/openvision-vit-so400m-patch14-84", "name": "OpenVision ViT-SoViT-400M/14 84", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9946, "dossier_id": "ucsc/openvision/openvision-vit-tiny-patch16-160", "name": "OpenVision ViT-Tiny/16 160", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9947, "dossier_id": "ucsc/openvision/openvision-vit-tiny-patch16-224", "name": "OpenVision ViT-Tiny/16 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9948, "dossier_id": "ucsc/openvision/openvision-vit-tiny-patch16-384", "name": "OpenVision ViT-Tiny/16 384", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9949, "dossier_id": "ucsc/openvision/openvision-vit-tiny-patch8-160", "name": "OpenVision ViT-Tiny/8 160", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9950, "dossier_id": "ucsc/openvision/openvision-vit-tiny-patch8-224", "name": "OpenVision ViT-Tiny/8 224", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9951, "dossier_id": "ucsc/openvision/openvision-vit-tiny-patch8-384", "name": "OpenVision ViT-Tiny/8 384", "maker": "ucsc", "family": "ucsc/openvision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9952, "dossier_id": "ucsc/openvision-2/openvision2-vit-giant-patch14-224-vision-only", "name": "OpenVision 2 ViT-Giant/14 224", "maker": "ucsc", "family": "ucsc/openvision-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9953, "dossier_id": "ucsc/openvision-2/openvision2-vit-huge-patch14-224-vision-only", "name": "OpenVision 2 ViT-Huge/14 224", "maker": "ucsc", "family": "ucsc/openvision-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9954, "dossier_id": "ucsc/openvision-2/openvision2-vit-huge-patch14-336-vision-only", "name": "OpenVision 2 ViT-Huge/14 336", "maker": "ucsc", "family": "ucsc/openvision-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9955, "dossier_id": "ucsc/openvision-2/openvision2-vit-huge-patch14-448-vision-only", "name": "OpenVision 2 ViT-Huge/14 448", "maker": "ucsc", "family": "ucsc/openvision-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9956, "dossier_id": "ucsc/openvision-2/openvision2-vit-large-patch14-224-vision-only", "name": "OpenVision 2 ViT-Large/14 224", "maker": "ucsc", "family": "ucsc/openvision-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9957, "dossier_id": "ucsc/openvision-2/openvision2-vit-large-patch14-336-vision-only", "name": "OpenVision 2 ViT-Large/14 336", "maker": "ucsc", "family": "ucsc/openvision-2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9958, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9959, "dossier_id": "ucsc/openvision-3/openvision3-vit-base-patch2-28", "name": "OpenVision 3 ViT-Base 224", "maker": "ucsc", "family": "ucsc/openvision-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9960, "dossier_id": "ucsc/openvision-3/openvision3-vit-base-patch2-32", "name": "OpenVision 3 ViT-Base 256", "maker": "ucsc", "family": "ucsc/openvision-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9961, "dossier_id": "ucsc/openvision-3/openvision3-vit-large-patch2-32", "name": "OpenVision 3 ViT-Large 256", "maker": "ucsc", "family": "ucsc/openvision-3", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9962, "dossier_id": "ucsc/recap-clip/recap-clip-vit-l-16", "name": "Recap-CLIP ViT-L/16", "maker": "ucsc", "family": "ucsc/recap-clip", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9963, "dossier_id": "ucsd/fastvideo/fasth3", "name": "FastH3", "maker": "ucsd", "family": "ucsd/fastvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9964, "dossier_id": "ucsd/fastvideo/fasthunyuan", "name": "FastHunyuan", "maker": "ucsd", "family": "ucsd/fastvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9965, "dossier_id": "ucsd/fastvideo/fastmochi", "name": "FastMochi", "maker": "ucsd", "family": "ucsd/fastvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9966, "dossier_id": "ucsd/musicldm/musicldm", "name": "MusicLDM", "maker": "ucsd", "family": "ucsd/musicldm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9967, "dossier_id": "ucsd/newt/newt", "name": "Newt", "maker": "ucsd", "family": "ucsd/newt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9968, "dossier_id": "ucsd/one-2-3-45/one-2-3-45", "name": "One-2-3-45", "maker": "ucsd", "family": "ucsd/one-2-3-45", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9969, "dossier_id": "ucsd/openshape/openshape", "name": "OpenShape", "maker": "ucsd", "family": "ucsd/openshape", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9970, "dossier_id": "ucsd/radbert/radbert-2m", "name": "RadBERT-2m", "maker": "ucsd", "family": "ucsd/radbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9971, "dossier_id": "ucsd/radbert/radbert-roberta-4m", "name": "RadBERT-RoBERTa-4m", "maker": "ucsd", "family": "ucsd/radbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9972, "dossier_id": "ucsd/td-mpc/td-mpc2", "name": "TD-MPC2", "maker": "ucsd", "family": "ucsd/td-mpc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9973, "dossier_id": "ucsd/zero123plus/zero123plus-depth-controlnet", "name": "Zero123++ depth ControlNet", "maker": "ucsd", "family": "ucsd/zero123plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9974, "dossier_id": "ucsd/zero123plus/zero123plus-normal-controlnet", "name": "Zero123++ normal ControlNet", "maker": "ucsd", "family": "ucsd/zero123plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9975, "dossier_id": "ucsd/zero123plus/zero123plus-v1-1", "name": "Zero123++ v1.1", "maker": "ucsd", "family": "ucsd/zero123plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9976, "dossier_id": "ucsd/zero123plus/zero123plus-v1-2", "name": "Zero123++ v1.2", "maker": "ucsd", "family": "ucsd/zero123plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9977, "dossier_id": "uct/wmt22/wmt22-southern-african", "name": "UCT WMT22", "maker": "uct", "family": "uct/wmt22", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9978, "dossier_id": "udel/arabict5/arabict5-49gb-base", "name": "ArabicT5-49GB-base", "maker": "udel", "family": "udel/arabict5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9979, "dossier_id": "udel/arabict5/arabict5-49gb-small", "name": "ArabicT5-49GB-small", "maker": "udel", "family": "udel/arabict5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9980, "dossier_id": "udel/cdsbert/cdsbert", "name": "cdsBERT", "maker": "udel", "family": "udel/cdsbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9981, "dossier_id": "udel/cdsbert/cdsbert-plus", "name": "cdsBERT+", "maker": "udel", "family": "udel/cdsbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9982, "dossier_id": "udio/udio/udio-130", "name": "Udio-130", "maker": "udio", "family": "udio/udio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9983, "dossier_id": "udio/udio/udio-32", "name": "Udio-32", "maker": "udio", "family": "udio/udio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9984, "dossier_id": "udio/udio/v1", "name": "Udio v1", "maker": "udio", "family": "udio/udio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9985, "dossier_id": "udio/udio/v1-5", "name": "Udio v1.5", "maker": "udio", "family": "udio/udio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9986, "dossier_id": "udio/udio/v1-5-allegro", "name": "Udio v1.5 Allegro", "maker": "udio", "family": "udio/udio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9987, "dossier_id": "uestc/atd/atd", "name": "ATD", "maker": "uestc", "family": "uestc/atd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9988, "dossier_id": "uestc/atd/atd-light", "name": "ATD-light", "maker": "uestc", "family": "uestc/atd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The authors use it for lightweight single-image super-resolution. Scales x2, x3, and x4 are versions of this product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9989, "dossier_id": "uestc/atd/atd-u", "name": "ATD-U", "maker": "uestc", "family": "uestc/atd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9990, "dossier_id": "uestc/geoquery/geoquery", "name": "GeoQuery", "maker": "uestc", "family": "uestc/geoquery", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9991, "dossier_id": "uestc/gtasr/gtasr", "name": "GTASR", "maker": "uestc", "family": "uestc/gtasr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9992, "dossier_id": "uestc/ig/internal-guidance", "name": "Internal Guidance", "maker": "uestc", "family": "uestc/ig", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9993, "dossier_id": "uestc/mia-vsr/mia-vsr", "name": "MIA-VSR", "maker": "uestc", "family": "uestc/mia-vsr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9994, "dossier_id": "uestc/mvar/mvar", "name": "MVAR", "maker": "uestc", "family": "uestc/mvar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9995, "dossier_id": "uestc/pft/pft", "name": "PFT", "maker": "uestc", "family": "uestc/pft", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9996, "dossier_id": "uestc/pft/pft-light", "name": "PFT-light", "maker": "uestc", "family": "uestc/pft", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9997, "dossier_id": "uestc/rdvq/rdvq", "name": "RDVQ", "maker": "uestc", "family": "uestc/rdvq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9998, "dossier_id": "uestc/tvqrap/tvqrap", "name": "TVQ&RAP", "maker": "uestc", "family": "uestc/tvqrap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 9999, "dossier_id": "uestc/ve-loss/ve-loss", "name": "VE-Loss", "maker": "uestc", "family": "uestc/ve-loss", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10000, "dossier_id": "ufl/gatortrongpt/gatortrongpt-20b", "name": "GatorTronGPT-20B", "maker": "ufl", "family": "ufl/gatortrongpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10001, "dossier_id": "ufl/gatortrongpt/gatortrongpt-5b", "name": "GatorTronGPT-5B", "maker": "ufl", "family": "ufl/gatortrongpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10002, "dossier_id": "ufl/medgpt/medgpt-oss", "name": "MedGPT-oss", "maker": "ufl", "family": "ufl/medgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10003, "dossier_id": "uiuc/coalm/coalm-405b", "name": "CoALM-405B", "maker": "uiuc", "family": "uiuc/coalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10004, "dossier_id": "uiuc/coalm/coalm-70b", "name": "CoALM-70B", "maker": "uiuc", "family": "uiuc/coalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same job as CoALM-8B at a larger scale: dialogue state tracking plus function calling.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10005, "dossier_id": "uiuc/coalm/coalm-8b", "name": "CoALM-8B", "maker": "uiuc", "family": "uiuc/coalm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10006, "dossier_id": "uiuc/graphvid/graphvid", "name": "GraphVid", "maker": "uiuc", "family": "uiuc/graphvid", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10007, "dossier_id": "uiuc/magicoder/magicoder-cl-7b", "name": "Magicoder-CL-7B", "maker": "uiuc", "family": "uiuc/magicoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10008, "dossier_id": "uiuc/magicoder/magicoder-ds-6-7b", "name": "Magicoder-DS-6.7B", "maker": "uiuc", "family": "uiuc/magicoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same code-generation job as the CodeLlama Magicoders, on a DeepSeek-Coder base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10009, "dossier_id": "uiuc/magicoder/magicoder-s-cl-7b", "name": "Magicoder-S-CL-7B", "maker": "uiuc", "family": "uiuc/magicoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10010, "dossier_id": "uiuc/magicoder/magicoder-s-ds-6-7b", "name": "Magicoder-S-DS-6.7B", "maker": "uiuc", "family": "uiuc/magicoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Code generation. The README reports HumanEval pass@1 of 76.8.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10011, "dossier_id": "uiuc/palm/palm", "name": "PALM", "maker": "uiuc", "family": "uiuc/palm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10012, "dossier_id": "uiuc/pyratok/pyratok", "name": "PyraTok", "maker": "uiuc", "family": "uiuc/pyratok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The GitHub says it should tokenize video for both understanding and generation, with language-aligned pyramidal quantization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10013, "dossier_id": "uiuc/rewardflow/rewardflow", "name": "RewardFlow", "maker": "uiuc", "family": "uiuc/rewardflow", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10014, "dossier_id": "ulb/arbock/arbock", "name": "ARBOCK", "maker": "ulb", "family": "ulb/arbock", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10015, "dossier_id": "ulb/digenic-effect/digenic-effect-predictor", "name": "Digenic Effect Predictor", "maker": "ulb", "family": "ulb/digenic-effect", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10016, "dossier_id": "ulb/hop/hop", "name": "HOP", "maker": "ulb", "family": "ulb/hop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10017, "dossier_id": "ulb/quate/quate-gene-pair", "name": "QuatE-based gene pair pathogenicity predictor", "maker": "ulb", "family": "ulb/quate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10018, "dossier_id": "ulb/varcopp/varcopp", "name": "VarCoPP", "maker": "ulb", "family": "ulb/varcopp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10019, "dossier_id": "ulm/cuts3d/cuts3d", "name": "CutS3D", "maker": "ulm", "family": "ulm/cuts3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10020, "dossier_id": "ulm/s2d/s2d", "name": "S2D", "maker": "ulm", "family": "ulm/s2d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10021, "dossier_id": "umass/revo/revo", "name": "ReVo", "maker": "umass", "family": "umass/revo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10022, "dossier_id": "umbc/compress/compress-alexnet", "name": "CompRess (AlexNet)", "maker": "umbc", "family": "umbc/compress", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10023, "dossier_id": "umbc/compress/compress-mobilenet", "name": "CompRess (MobileNet)", "maker": "umbc", "family": "umbc/compress", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10024, "dossier_id": "umbc/compress/compress-resnet18", "name": "CompRess (ResNet-18)", "maker": "umbc", "family": "umbc/compress", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10025, "dossier_id": "umbc/compress/compress-resnet50", "name": "CompRess (ResNet-50)", "maker": "umbc", "family": "umbc/compress", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10026, "dossier_id": "umbc/msf/msf-resnet50", "name": "MSF (ResNet-50)", "maker": "umbc", "family": "umbc/msf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10027, "dossier_id": "umd/csd/csd-vit-l", "name": "CSD ViT-L", "maker": "umd", "family": "umd/csd", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10028, "dossier_id": "umd/sphere-encoder/sphere-l-af", "name": "Sphere-L Animal-Faces", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10029, "dossier_id": "umd/sphere-encoder/sphere-l-imagenet", "name": "Sphere-L ImageNet", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10030, "dossier_id": "umd/sphere-encoder/sphere-l-of", "name": "Sphere-L Oxford-Flowers", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10031, "dossier_id": "umd/sphere-encoder/sphere-xl-imagenet", "name": "Sphere-XL ImageNet", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10032, "dossier_id": "umd/sphere-encoder/sphere2-b-flowers-256", "name": "Sphere2-B Flowers 256", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10033, "dossier_id": "umd/sphere-encoder/sphere2-b-flowers-512", "name": "Sphere2-B Flowers 512", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10034, "dossier_id": "umd/sphere-encoder/sphere2-b-imagenet-256", "name": "Sphere2-B ImageNet 256", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10035, "dossier_id": "umd/sphere-encoder/sphere2-b-imagenet-512", "name": "Sphere2-B ImageNet 512", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10036, "dossier_id": "umd/sphere-encoder/sphere2-l-flowers-256", "name": "Sphere2-L Flowers 256", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10037, "dossier_id": "umd/sphere-encoder/sphere2-l-flowers-512", "name": "Sphere2-L Flowers 512", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10038, "dossier_id": "umd/sphere-encoder/sphere2-l-imagenet-256", "name": "Sphere2-L ImageNet 256", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10039, "dossier_id": "umd/sphere-encoder/sphere2-l-imagenet-512", "name": "Sphere2-L ImageNet 512", "maker": "umd", "family": "umd/sphere-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10040, "dossier_id": "umich/deepglioma/deepglioma", "name": "DeepGlioma", "maker": "umich", "family": "umich/deepglioma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10041, "dossier_id": "umich/fastglioma/fastglioma", "name": "FastGlioma", "maker": "umich", "family": "umich/fastglioma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10042, "dossier_id": "umich/neurovfm/neurovfm", "name": "NeuroVFM", "maker": "umich", "family": "umich/neurovfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10043, "dossier_id": "umich/prima/prima", "name": "Prima", "maker": "umich", "family": "umich/prima", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10044, "dossier_id": "umich/virtex/virtex", "name": "VirTex", "maker": "umich", "family": "umich/virtex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10045, "dossier_id": "umn/sansi/sansi", "name": "SanSi", "maker": "umn", "family": "umn/sansi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10046, "dossier_id": "umn/sansi/sansi-2-6b", "name": "SanSi-2.6B", "maker": "umn", "family": "umn/sansi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10047, "dossier_id": "unam/ciempiess/ciempiess-spanish-models", "name": "CIEMPIESS Spanish Models", "maker": "unam", "family": "unam/ciempiess", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10048, "dossier_id": "unam/ladagan/ladagan", "name": "LadaGAN", "maker": "unam", "family": "unam/ladagan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10049, "dossier_id": "unbabel/tower-plus/tower-plus-72b", "name": "Tower+ 72B", "maker": "unbabel", "family": "unbabel/tower-plus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10050, "dossier_id": "unbabel/tower-v2/tower-v2-70b", "name": "Tower-v2 70B", "maker": "unbabel", "family": "unbabel/tower-v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10051, "dossier_id": "unbabel/tower-v2/tower-v2-7b", "name": "Tower-v2 7B", "maker": "unbabel", "family": "unbabel/tower-v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10052, "dossier_id": "unbabel/towerllm/towerllm", "name": "TowerLLM", "maker": "unbabel", "family": "unbabel/towerllm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10053, "dossier_id": "unc/aimnet/aimnet", "name": "AIMNet", "maker": "unc", "family": "unc/aimnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10054, "dossier_id": "unc/ani/ani-1", "name": "ANI-1", "maker": "unc", "family": "unc/ani", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10055, "dossier_id": "unc/ani/ani-1ccx", "name": "ANI-1ccx", "maker": "unc", "family": "unc/ani", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10056, "dossier_id": "unc/ani/ani-1x", "name": "ANI-1x", "maker": "unc", "family": "unc/ani", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10057, "dossier_id": "unc/bifrost/bifrost-1", "name": "Bifrost-1", "maker": "unc", "family": "unc/bifrost", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10058, "dossier_id": "unc/codi/codi", "name": "CoDi", "maker": "unc", "family": "unc/codi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10059, "dossier_id": "unc/gradicon/multigradicon", "name": "multiGradICON", "maker": "unc", "family": "unc/gradicon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10060, "dossier_id": "unc/gradicon/unicarl", "name": "uniCARL", "maker": "unc", "family": "unc/gradicon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10061, "dossier_id": "unc/gradicon/unigradicon", "name": "uniGradICON", "maker": "unc", "family": "unc/gradicon", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10062, "dossier_id": "unc/tvlt/tvlt", "name": "TVLT", "maker": "unc", "family": "unc/tvlt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10063, "dossier_id": "unc/vl/vl-bart", "name": "VL-BART", "maker": "unc", "family": "unc/vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10064, "dossier_id": "unc/vl/vl-t5", "name": "VL-T5", "maker": "unc", "family": "unc/vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10065, "dossier_id": "unil/mixmhc2pred/mixmhc2pred", "name": "MixMHC2pred 2.1", "maker": "unil", "family": "unil/mixmhc2pred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10066, "dossier_id": "unil/mixmhcpred/mixmhcpred", "name": "MixMHCpred 3.0", "maker": "unil", "family": "unil/mixmhcpred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10067, "dossier_id": "unil/mixtcrpred/mixtcrpred", "name": "MixTCRpred 1.0", "maker": "unil", "family": "unil/mixtcrpred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10068, "dossier_id": "unil/phosmhcpred/phosmhcpred", "name": "PhosMHCpred", "maker": "unil", "family": "unil/phosmhcpred", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10069, "dossier_id": "unil/prime/prime", "name": "PRIME 2.1", "maker": "unil", "family": "unil/prime", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10070, "dossier_id": "unimi/m5c/rna-m5c-predictor", "name": "RNA m\u2075C Predictor", "maker": "unimi", "family": "unimi/m5c", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Milan AnacletoLab model RNA m\u2075C Predictor (rna-m5c-predictor). Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10071, "dossier_id": "unitn/composes/composes-count", "name": "COMPOSES Count", "maker": "unitn", "family": "unitn/composes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10072, "dossier_id": "unitn/composes/composes-count-svd", "name": "COMPOSES Count SVD", "maker": "unitn", "family": "unitn/composes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10073, "dossier_id": "unitn/composes/composes-predict", "name": "COMPOSES Predict", "maker": "unitn", "family": "unitn/composes", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10074, "dossier_id": "unitn/dm/typedm", "name": "TypeDM", "maker": "unitn", "family": "unitn/dm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10075, "dossier_id": "unito/anatcl/anatcl", "name": "AnatCL", "maker": "unito", "family": "unito/anatcl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10076, "dossier_id": "unito/ddpm-pcc/ddpm-pcc", "name": "DDPM-PCC", "maker": "unito", "family": "unito/ddpm-pcc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10077, "dossier_id": "unito/medsae/medsae", "name": "MedSAE", "maker": "unito", "family": "unito/medsae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10078, "dossier_id": "unito/wignet/wignet", "name": "WiGNet", "maker": "unito", "family": "unito/wignet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10079, "dossier_id": "unlv/ecpick/ecpick", "name": "ECPICK", "maker": "unlv", "family": "unlv/ecpick", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10080, "dossier_id": "unlv/hit-ec/hit-ec", "name": "HIT-EC", "maker": "unlv", "family": "unlv/hit-ec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10081, "dossier_id": "unlv/idf-ec/idf-ec", "name": "IDF-EC", "maker": "unlv", "family": "unlv/idf-ec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10082, "dossier_id": "unsw/darwin/darwin-1-5-7b", "name": "DARWIN 1.5", "maker": "unsw", "family": "unsw/darwin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10083, "dossier_id": "unsw/lumi/lumi-transformer", "name": "Lumi-Transformer", "maker": "unsw", "family": "unsw/lumi", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10084, "dossier_id": "upc/festcat/festcat-bet", "name": "FestCat Bet", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10085, "dossier_id": "upc/festcat/festcat-eli", "name": "FestCat Eli", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10086, "dossier_id": "upc/festcat/festcat-eva", "name": "FestCat Eva", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10087, "dossier_id": "upc/festcat/festcat-jan", "name": "FestCat Jan", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10088, "dossier_id": "upc/festcat/festcat-mar", "name": "FestCat Mar", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10089, "dossier_id": "upc/festcat/festcat-ona", "name": "FestCat Ona", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10090, "dossier_id": "upc/festcat/festcat-pau", "name": "FestCat Pau", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10091, "dossier_id": "upc/festcat/festcat-pep", "name": "FestCat Pep", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10092, "dossier_id": "upc/festcat/festcat-pol", "name": "FestCat Pol", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10093, "dossier_id": "upc/festcat/festcat-teo", "name": "FestCat Teo", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10094, "dossier_id": "upc/festcat/festcat-uri", "name": "FestCat Uri", "maker": "upc", "family": "upc/festcat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10095, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10096, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10097, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10098, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10099, "dossier_id": "upc/shas/shas", "name": "SHAS", "maker": "upc", "family": "upc/shas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10100, "dossier_id": "upc/zeroswot/zeroswot-large-asr-cv-en-to-200", "name": "ZeroSwot-Large ASR CommonVoice en-to-200", "maker": "upc", "family": "upc/zeroswot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10101, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10102, "dossier_id": "upc/zeroswot/zeroswot-large-asr-mustc-en-to-200", "name": "ZeroSwot-Large ASR MuST-C en-to-200", "maker": "upc", "family": "upc/zeroswot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10103, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10104, "dossier_id": "upc/zeroswot/zeroswot-medium-asr-cv-en-to-200", "name": "ZeroSwot-Medium ASR CommonVoice en-to-200", "maker": "upc", "family": "upc/zeroswot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10105, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10106, "dossier_id": "upc/zeroswot/zeroswot-medium-asr-mustc-en-to-200", "name": "ZeroSwot-Medium ASR MuST-C en-to-200", "maker": "upc", "family": "upc/zeroswot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10107, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10108, "dossier_id": "upf/allmusiccaps/allmusiccaps-baseline", "name": "AllMusicCaps baseline", "maker": "upf", "family": "upf/allmusiccaps", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10109, "dossier_id": "upf/allmusiccaps/allmusiccaps-lejepa", "name": "AllMusicCaps LeJEPA", "maker": "upf", "family": "upf/allmusiccaps", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "LeJEPA training objective, described on the card as negative-free, invariance plus SigReg. Same 512-d music-text embedding setup.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10110, "dossier_id": "upf/approachability/approachability-2c", "name": "Approachability 2-class", "maker": "upf", "family": "upf/approachability", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10111, "dossier_id": "upf/approachability/approachability-3c", "name": "Approachability 3-class", "maker": "upf", "family": "upf/approachability", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same approachability task with three levels. Label names are not on the page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10112, "dossier_id": "upf/approachability/approachability-regression", "name": "Approachability regression", "maker": "upf", "family": "upf/approachability", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Continuous approachability score on Discogs-EffNet. Output layer in the example is model/Identity.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10113, "dossier_id": "upf/arousal-valence/deam", "name": "DEAM arousal-valence", "maker": "upf", "family": "upf/arousal-valence", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Valence and arousal regression on the DEAM dataset. Two dimensions, range [1, 9], order valence then arousal.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10114, "dossier_id": "upf/arousal-valence/emomusic", "name": "emoMusic arousal-valence", "maker": "upf", "family": "upf/arousal-valence", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Valence and arousal regression on the emoMusic dataset. Two dimensions, range [1, 9].", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10115, "dossier_id": "upf/arousal-valence/muse", "name": "MuSE arousal-valence", "maker": "upf", "family": "upf/arousal-valence", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10116, "dossier_id": "upf/autotag/msd-top50", "name": "MSD top-50 tags", "maker": "upf", "family": "upf/autotag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10117, "dossier_id": "upf/autotag/mtg-jamendo-top50tags", "name": "MTG-Jamendo top-50 tags", "maker": "upf", "family": "upf/autotag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10118, "dossier_id": "upf/autotag/mtt", "name": "MagnaTagATune top-50", "maker": "upf", "family": "upf/autotag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Automatic tagging with the top 50 MagnaTagATune tags.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10119, "dossier_id": "upf/danceability/danceability", "name": "Danceability", "maker": "upf", "family": "upf/danceability", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class danceability: danceable, not_danceable.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10120, "dossier_id": "upf/discogs-effnet/discogs-effnet", "name": "Discogs-EffNet", "maker": "upf", "family": "upf/discogs-effnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10121, "dossier_id": "upf/discogs-effnet/discogs-effnet-artist", "name": "Discogs-EffNet artist", "maker": "upf", "family": "upf/discogs-effnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contrastive Discogs-EffNet trained so that tracks from the same artist embed near each other.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10122, "dossier_id": "upf/discogs-effnet/discogs-effnet-label", "name": "Discogs-EffNet label", "maker": "upf", "family": "upf/discogs-effnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contrastive Discogs-EffNet trained on record-label associations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10123, "dossier_id": "upf/discogs-effnet/discogs-effnet-multi", "name": "Discogs-EffNet multi", "maker": "upf", "family": "upf/discogs-effnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contrastive Discogs-EffNet trained on artist and track associations together. The page calls this the multi target.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10124, "dossier_id": "upf/discogs-effnet/discogs-effnet-release", "name": "Discogs-EffNet release", "maker": "upf", "family": "upf/discogs-effnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contrastive Discogs-EffNet trained on release (album) associations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10125, "dossier_id": "upf/discogs-effnet/discogs-effnet-track", "name": "Discogs-EffNet track", "maker": "upf", "family": "upf/discogs-effnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Contrastive Discogs-EffNet trained on track-level self-supervised associations.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10126, "dossier_id": "upf/engagement/engagement-2c", "name": "Engagement 2-class", "maker": "upf", "family": "upf/engagement", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Whether music asks for active attention versus background listening. Two levels. Label names are not on the page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10127, "dossier_id": "upf/engagement/engagement-3c", "name": "Engagement 3-class", "maker": "upf", "family": "upf/engagement", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same engagement task with three levels.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10128, "dossier_id": "upf/engagement/engagement-regression", "name": "Engagement regression", "maker": "upf", "family": "upf/engagement", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Continuous engagement score on Discogs-EffNet.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10129, "dossier_id": "upf/fsd-sinet/fsd-sinet", "name": "FSD-SINet", "maker": "upf", "family": "upf/fsd-sinet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Audio event recognition and embedding extraction for 200 AudioSet-ontology classes drawn from FSD50K.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10130, "dossier_id": "upf/genre/genre-discogs400", "name": "Genre Discogs400", "maker": "upf", "family": "upf/genre", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10131, "dossier_id": "upf/genre/genre-discogs519", "name": "Genre Discogs519", "maker": "upf", "family": "upf/genre", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-label music-style classification over 519 Discogs styles, on the updated MAEST 30-second 519-label embedding.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10132, "dossier_id": "upf/instrument/fs-loop-ds", "name": "Freesound loop role", "maker": "upf", "family": "upf/instrument", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instrument role of music loops from the Freesound Loop Dataset. Five classes: bass, chords, fx, melody, percussion.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10133, "dossier_id": "upf/instrument/mtg-jamendo-instrument", "name": "MTG-Jamendo instrument", "maker": "upf", "family": "upf/instrument", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-label instrument tags, 40 classes from MTG-Jamendo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10134, "dossier_id": "upf/jamendo/mtg-jamendo-genre", "name": "MTG-Jamendo genre", "maker": "upf", "family": "upf/jamendo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-label genre tagging on the 87-class genre subset of the MTG-Jamendo dataset.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10135, "dossier_id": "upf/jamendo/mtg-jamendo-moodtheme", "name": "MTG-Jamendo mood and theme", "maker": "upf", "family": "upf/jamendo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-label mood and theme tags, 56 classes from MTG-Jamendo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10136, "dossier_id": "upf/maest/maest", "name": "MAEST", "maker": "upf", "family": "upf/maest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10137, "dossier_id": "upf/mood/mood-acoustic", "name": "Mood acoustic", "maker": "upf", "family": "upf/mood", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class sound type: acoustic, non_acoustic. Listed by Essentia under instrumentation even though the name says mood.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10138, "dossier_id": "upf/mood/mood-aggressive", "name": "Mood aggressive", "maker": "upf", "family": "upf/mood", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class mood: aggressive, non_aggressive.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10139, "dossier_id": "upf/mood/mood-electronic", "name": "Mood electronic", "maker": "upf", "family": "upf/mood", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class sound type: electronic, non_electronic.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10140, "dossier_id": "upf/mood/mood-happy", "name": "Mood happy", "maker": "upf", "family": "upf/mood", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class mood: happy, non_happy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10141, "dossier_id": "upf/mood/mood-party", "name": "Mood party", "maker": "upf", "family": "upf/mood", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class mood: party, non_party.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10142, "dossier_id": "upf/mood/mood-relaxed", "name": "Mood relaxed", "maker": "upf", "family": "upf/mood", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class mood: relaxed, non_relaxed.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10143, "dossier_id": "upf/mood/moods-mirex", "name": "Moods MIREX", "maker": "upf", "family": "upf/mood", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Five MIREX audio-mood clusters. The cluster adjective lists are on the Essentia page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10144, "dossier_id": "upf/musicnn/msd-musicnn", "name": "MSD-MusiCNN", "maker": "upf", "family": "upf/musicnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10145, "dossier_id": "upf/nsynth/nsynth-acoustic-electronic", "name": "NSynth acoustic / electronic", "maker": "upf", "family": "upf/nsynth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Monophonic source origin on NSynth. Two classes: acoustic, electronic.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10146, "dossier_id": "upf/nsynth/nsynth-bright-dark", "name": "NSynth bright / dark", "maker": "upf", "family": "upf/nsynth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Monophonic timbre color on NSynth. Two classes: bright, dark.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10147, "dossier_id": "upf/nsynth/nsynth-instrument", "name": "NSynth instrument", "maker": "upf", "family": "upf/nsynth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Monophonic instrument family on NSynth. Eleven classes: mallet, string, reed, guitar, synth_lead, vocal, bass, flute, keyboard, brass, organ.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10148, "dossier_id": "upf/nsynth/nsynth-reverb", "name": "NSynth reverb", "maker": "upf", "family": "upf/nsynth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Reverb detection on monophonic NSynth sources. Two classes: dry, wet.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10149, "dossier_id": "upf/omar-rq/omar-rq-base", "name": "OMAR-RQ base", "maker": "upf", "family": "upf/omar-rq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10150, "dossier_id": "upf/omar-rq/omar-rq-base-freesound-large", "name": "OMAR-RQ Freesound large", "maker": "upf", "family": "upf/omar-rq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Freesound-trained encoder for general sound research. The card says it follows the large architecture using the multicodebook approach.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10151, "dossier_id": "upf/omar-rq/omar-rq-base-freesound-small", "name": "OMAR-RQ Freesound small", "maker": "upf", "family": "upf/omar-rq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10152, "dossier_id": "upf/omar-rq/omar-rq-multicodebook", "name": "OMAR-RQ multicodebook", "maker": "upf", "family": "upf/omar-rq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mel-spectrogram encoder at 15.63 Hz with a multicodebook target. Best tagging mAP in the vendor table (0.488).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10153, "dossier_id": "upf/omar-rq/omar-rq-multifeature", "name": "OMAR-RQ multifeature", "maker": "upf", "family": "upf/omar-rq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Waveform multifeature encoder at 18.75 Hz.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10154, "dossier_id": "upf/omar-rq/omar-rq-multifeature-25hz", "name": "OMAR-RQ multifeature 25 Hz", "maker": "upf", "family": "upf/omar-rq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Waveform multifeature encoder at 25 Hz without finite scalar quantization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10155, "dossier_id": "upf/omar-rq/omar-rq-multifeature-25hz-fsq", "name": "OMAR-RQ multifeature 25 Hz FSQ", "maker": "upf", "family": "upf/omar-rq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10156, "dossier_id": "upf/omar-rq/omar-rq-multifeature-78m", "name": "OMAR-RQ multifeature 78M", "maker": "upf", "family": "upf/omar-rq", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10157, "dossier_id": "upf/pdf-difficulty/pdf-difficulty", "name": "PDF Difficulty", "maker": "upf", "family": "upf/pdf-difficulty", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10158, "dossier_id": "upf/soccerhigh/soccerhigh", "name": "SoccerHigh baselines", "maker": "upf", "family": "upf/soccerhigh", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Automatic soccer video summarization baselines from the SoccerHigh benchmark paper. The card lists four encoder variants.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10159, "dossier_id": "upf/timbre/timbre", "name": "Timbre", "maker": "upf", "family": "upf/timbre", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class timbre color: bright, dark. Only a Discogs-EffNet head is shipped.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10160, "dossier_id": "upf/tonality/tonal-atonal", "name": "Tonal / atonal", "maker": "upf", "family": "upf/tonality", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class tonality: atonal, tonal.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10161, "dossier_id": "upf/voice/voice-gender", "name": "Voice gender", "maker": "upf", "family": "upf/voice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Singing-voice gender, two classes: female, male. Weight prefix is gender, not voice_gender.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10162, "dossier_id": "upf/voice/voice-instrumental", "name": "Voice / instrumental", "maker": "upf", "family": "upf/voice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Two-class presence of voice: instrumental, voice.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10163, "dossier_id": "uppsala/attentionhtr/attentionhtr", "name": "AttentionHTR", "maker": "uppsala", "family": "uppsala/attentionhtr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10164, "dossier_id": "uppsala/malt/engmalt", "name": "engmalt", "maker": "uppsala", "family": "uppsala/malt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10165, "dossier_id": "uppsala/malt/fremalt", "name": "fremalt", "maker": "uppsala", "family": "uppsala/malt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10166, "dossier_id": "uppsala/malt/swemalt", "name": "swemalt", "maker": "uppsala", "family": "uppsala/malt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10167, "dossier_id": "uppsala/ptp/ptp", "name": "PTP", "maker": "uppsala", "family": "uppsala/ptp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10168, "dossier_id": "upstage/classify/document-classify", "name": "Document Classify", "maker": "upstage", "family": "upstage/classify", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Classify scans and digital files into user-defined categories, including split bundles, without templates or fine-tuning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10169, "dossier_id": "upstage/document-parse/document-parse", "name": "Document Parse", "maker": "upstage", "family": "upstage/document-parse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10170, "dossier_id": "upstage/extract/information-extract", "name": "Information Extract", "maker": "upstage", "family": "upstage/extract", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10171, "dossier_id": "upstage/ocr/document-ocr", "name": "Document OCR", "maker": "upstage", "family": "upstage/ocr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Read text from tilted, low-quality, or multilingual document images, with a confidence score.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10172, "dossier_id": "upstage/prebuilt/air-waybill-extraction", "name": "Air Waybill Extraction", "maker": "upstage", "family": "upstage/prebuilt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Extract predefined fields from air waybills: parties, routing, charges, and cargo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10173, "dossier_id": "upstage/prebuilt/bill-of-lading-and-shipping-request-extraction", "name": "Bill of Lading and Shipping Request Extraction", "maker": "upstage", "family": "upstage/prebuilt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Extract predefined logistics fields from bills of lading and shipping requests.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10174, "dossier_id": "upstage/prebuilt/commercial-invoice-and-packing-list-extraction", "name": "Commercial Invoice and Packing List Extraction", "maker": "upstage", "family": "upstage/prebuilt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Extract predefined trade fields from commercial invoices and packing lists.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10175, "dossier_id": "upstage/prebuilt/kr-export-declaration-certificate-extraction", "name": "Korea Export Declaration Certificate Extraction", "maker": "upstage", "family": "upstage/prebuilt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Extract predefined fields from Korean export declaration certificates.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10176, "dossier_id": "upstage/prebuilt/receipt-extraction", "name": "Receipt Extraction", "maker": "upstage", "family": "upstage/prebuilt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Extract store, line-item, tax, and payment fields from paper receipts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10177, "dossier_id": "upstage/solar/solar-10-7b", "name": "SOLAR-10.7B", "maker": "upstage", "family": "upstage/solar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10178, "dossier_id": "upstage/solar/solar-10-7b-instruct", "name": "SOLAR-10.7B-Instruct", "maker": "upstage", "family": "upstage/solar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is the single-turn conversational fine-tune of SOLAR-10.7B.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10179, "dossier_id": "upstage/solar/solar-decide", "name": "Solar Decide", "maker": "upstage", "family": "upstage/solar", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Routing, classification, and policy checks over a long state, including a full document, without writing an answer.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10180, "dossier_id": "upstage/solar/solar-open-100b", "name": "Solar Open 100B", "maker": "upstage", "family": "upstage/solar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card describes a from-scratch bilingual model for reasoning, instruction following, and tool use.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10181, "dossier_id": "upstage/solar/solar-open-2", "name": "Solar Open 2", "maker": "upstage", "family": "upstage/solar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The model card says it is built for agent work: office tasks, documents, and coding, in English, Korean, and Japanese.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10182, "dossier_id": "upstage/solar/solar-pro-preview", "name": "Solar Pro Preview", "maker": "upstage", "family": "upstage/solar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10183, "dossier_id": "upstage/tinysolar/tinysolar-111m-4k", "name": "TinySolar 111M 4K", "maker": "upstage", "family": "upstage/tinysolar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller TinySolar weight release. Hugging Face createdAt 2026-05-27. Card not quoted.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10184, "dossier_id": "upstage/tinysolar/tinysolar-187m-4k", "name": "TinySolar 187M 4K", "maker": "upstage", "family": "upstage/tinysolar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "187M weight release. Hugging Face createdAt 2026-05-27. Card not quoted.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10185, "dossier_id": "upstage/tinysolar/tinysolar-248m-4k", "name": "TinySolar 248M 4K", "maker": "upstage", "family": "upstage/tinysolar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Base 248M release. Card body is empty aside from Apache-2.0. HF reports about 0.2B parameters.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10186, "dossier_id": "upstage/tinysolar/tinysolar-248m-4k-py", "name": "TinySolar 248M 4K Python", "maker": "upstage", "family": "upstage/tinysolar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Python-oriented 248M release. Card not separately quoted; Hugging Face lists Apache-2.0.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10187, "dossier_id": "upstage/tinysolar/tinysolar-248m-4k-py-instruct", "name": "TinySolar 248M 4K Python Instruct", "maker": "upstage", "family": "upstage/tinysolar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Instruct variant. Card body not retrieved beyond the Hugging Face listing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10188, "dossier_id": "upstage/translation/translation-enko", "name": "Translation EN-KO", "maker": "upstage", "family": "upstage/translation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "English-to-Korean translation, including tone carried over from earlier turns and formatted text such as subtitles.", "summary_intro": "Translation EN-KO is a retired Upstage translation model, collected 2026-10-15. Upstage deprecated all translation APIs on 9 June 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10189, "dossier_id": "upstage/translation/translation-koen", "name": "Translation KO-EN", "maker": "upstage", "family": "upstage/translation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Korean-to-English translation, including formatted subtitle text.", "summary_intro": "Translation KO-EN is a retired Upstage translation model, collected 2026-10-15. Upstage deprecated all translation APIs on 9 June 2025.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10190, "dossier_id": "uq/autobool/autobool-qwen3-4b", "name": "AutoBool Qwen3 4B", "maker": "uq", "family": "uq/autobool", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10191, "dossier_id": "uq/bitag/bitag-t5-large", "name": "BiTAG T5-Large", "maker": "uq", "family": "uq/bitag", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10192, "dossier_id": "uq/diffretriever/diffretriever-dream-7b", "name": "DiffRetriever Dream 7B", "maker": "uq", "family": "uq/diffretriever", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10193, "dossier_id": "uq/rank-r1/rank-r1-14b", "name": "Rank-R1 14B", "maker": "uq", "family": "uq/rank-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10194, "dossier_id": "uq/rank-r1/rank-r1-32b", "name": "Rank-R1 32B", "maker": "uq", "family": "uq/rank-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10195, "dossier_id": "uq/rank-r1/rank-r1-3b", "name": "Rank-R1 3B", "maker": "uq", "family": "uq/rank-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10196, "dossier_id": "uq/rank-r1/rank-r1-7b", "name": "Rank-R1 7B", "maker": "uq", "family": "uq/rank-r1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10197, "dossier_id": "uq/setwise/setwise-sft-14b", "name": "Setwise-SFT 14B", "maker": "uq", "family": "uq/setwise", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10198, "dossier_id": "uq/setwise/setwise-sft-3b", "name": "Setwise-SFT 3B", "maker": "uq", "family": "uq/setwise", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10199, "dossier_id": "uq/setwise/setwise-sft-7b", "name": "Setwise-SFT 7B", "maker": "uq", "family": "uq/setwise", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10200, "dossier_id": "uq/starbucks/starbucks-msmarco", "name": "Starbucks MS MARCO", "maker": "uq", "family": "uq/starbucks", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10201, "dossier_id": "uq/starbucks/starbucks-smae", "name": "Starbucks SMAE", "maker": "uq", "family": "uq/starbucks", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10202, "dossier_id": "uq/toroder/toroder", "name": "ToRoDer", "maker": "uq", "family": "uq/toroder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10203, "dossier_id": "uq/toroder/toroder-msmarco", "name": "ToRoDer MS MARCO", "maker": "uq", "family": "uq/toroder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10204, "dossier_id": "usc/pifu/pifu", "name": "PIFu", "maker": "usc", "family": "usc/pifu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10205, "dossier_id": "usc/uh-1/uh-1", "name": "UH-1", "maker": "usc", "family": "usc/uh-1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10206, "dossier_id": "usp/nilc-embeddings/nilc-fasttext", "name": "NILC FastText", "maker": "usp", "family": "usp/nilc-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10207, "dossier_id": "usp/nilc-embeddings/nilc-glove", "name": "NILC GloVe", "maker": "usp", "family": "usp/nilc-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10208, "dossier_id": "usp/nilc-embeddings/nilc-wang2vec", "name": "NILC Wang2Vec", "maker": "usp", "family": "usp/nilc-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10209, "dossier_id": "usp/nilc-embeddings/nilc-word2vec", "name": "NILC Word2Vec", "maker": "usp", "family": "usp/nilc-embeddings", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10210, "dossier_id": "ustc/d-fine/d-fine-l", "name": "D-FINE-L", "maker": "ustc", "family": "ustc/d-fine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10211, "dossier_id": "ustc/d-fine/d-fine-m", "name": "D-FINE-M", "maker": "ustc", "family": "ustc/d-fine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10212, "dossier_id": "ustc/d-fine/d-fine-n", "name": "D-FINE-N", "maker": "ustc", "family": "ustc/d-fine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10213, "dossier_id": "ustc/d-fine/d-fine-s", "name": "D-FINE-S", "maker": "ustc", "family": "ustc/d-fine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10214, "dossier_id": "ustc/d-fine/d-fine-x", "name": "D-FINE-X", "maker": "ustc", "family": "ustc/d-fine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10215, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10216, "dossier_id": "ustc/kale-lm/llama3-kale-lm-chem-8b", "name": "Llama3-KALE-LM-Chem-8B", "maker": "ustc", "family": "ustc/kale-lm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10217, "dossier_id": "ustc/mcd-tsf/mcd-tsf", "name": "MCD-TSF", "maker": "ustc", "family": "ustc/mcd-tsf", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10218, "dossier_id": "ustc/socraticlm/socraticlm", "name": "SocraticLM", "maker": "ustc", "family": "ustc/socraticlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10219, "dossier_id": "ustc/socraticlm/socraticlm-glm-4-9b", "name": "SocraticLM GLM-4-9B-Chat demo", "maker": "ustc", "family": "ustc/socraticlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10220, "dossier_id": "ustc/socraticlm/socraticlm-llama-3-1-8b", "name": "SocraticLM Llama-3.1-8B-Instruct demo", "maker": "ustc", "family": "ustc/socraticlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10221, "dossier_id": "ut-austin/coco/coco-chronos-2", "name": "CoCo Chronos-2", "maker": "ut-austin", "family": "ut-austin/coco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10222, "dossier_id": "ut-austin/coco/coco-qwen3-4b", "name": "CoCo Qwen3-4B", "maker": "ut-austin", "family": "ut-austin/coco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10223, "dossier_id": "ut-austin/ript-vla/ript-vla", "name": "RIPT-VLA", "maker": "ut-austin", "family": "ut-austin/ript-vla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10224, "dossier_id": "ut-austin/vlm-3r/vlm-3r", "name": "VLM-3R", "maker": "ut-austin", "family": "ut-austin/vlm-3r", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10225, "dossier_id": "uta/tcl/tcl", "name": "Triple Contrastive Learning", "maker": "uta", "family": "uta/tcl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10226, "dossier_id": "utd/msmformer/msmformer", "name": "Mean Shift Mask Transformer", "maker": "utd", "family": "utd/msmformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10227, "dossier_id": "utrecht/chimp-vocalization/chimp-vocalization-cnn10-sanctuary", "name": "Chimpanzee Vocalization CNN10 (sanctuary)", "maker": "utrecht", "family": "utrecht/chimp-vocalization", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10228, "dossier_id": "utrecht/chimp-vocalization/chimp-vocalization-cnn10-synthetic", "name": "Chimpanzee Vocalization CNN10 (synthetic)", "maker": "utrecht", "family": "utrecht/chimp-vocalization", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10229, "dossier_id": "utrecht/chimp-vocalization/chimp-vocalization-cnn12-sanctuary", "name": "Chimpanzee Vocalization CNN12 (sanctuary)", "maker": "utrecht", "family": "utrecht/chimp-vocalization", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10230, "dossier_id": "utrecht/chimp-vocalization/chimp-vocalization-cnn12-synthetic", "name": "Chimpanzee Vocalization CNN12 (synthetic)", "maker": "utrecht", "family": "utrecht/chimp-vocalization", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10231, "dossier_id": "utrecht/olifant/olifant-edufineweb-chatbot", "name": "Olifant EduFineWeb Chatbot", "maker": "utrecht", "family": "utrecht/olifant", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10232, "dossier_id": "utsa/hydro/hydro-swin-v2-msi", "name": "Hydro Swin v2 MSI", "maker": "utsa", "family": "utsa/hydro", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10233, "dossier_id": "utsa/hydro/hydro-swin-v2-rgb", "name": "Hydro Swin v2 RGB", "maker": "utsa", "family": "utsa/hydro", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10234, "dossier_id": "utwente/cabinet/cabinet-mobilenetv3-large-aeroscapes", "name": "CABiNet MobileNetV3-Large AeroScapes", "maker": "utwente", "family": "utwente/cabinet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10235, "dossier_id": "utwente/cabinet/cabinet-mobilenetv3-large-uavid", "name": "CABiNet MobileNetV3-Large UAVid", "maker": "utwente", "family": "utwente/cabinet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10236, "dossier_id": "utwente/cabinet/cabinet-mobilenetv3-large-vdd", "name": "CABiNet MobileNetV3-Large VDD", "maker": "utwente", "family": "utwente/cabinet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10237, "dossier_id": "utwente/cabinet/cabinet-mobilenetv3-small-uavid", "name": "CABiNet MobileNetV3-Small UAVid", "maker": "utwente", "family": "utwente/cabinet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10238, "dossier_id": "utwente/glavitu/glavitu", "name": "GlaViTU", "maker": "utwente", "family": "utwente/glavitu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Twente Faculty ITC model GlaViTU (Glacier-VisionTransformer-U-Net) for global glacier mapping. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10239, "dossier_id": "utwente/glavitu/glavitu-igarss", "name": "GlaViTU IGARSS 2023", "maker": "utwente", "family": "utwente/glavitu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "University of Twente Faculty ITC glacier-mapping model GlaViTU, the IGARSS 2023 release. Status: active. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10240, "dossier_id": "uvm/ampgan/ampgan-v2", "name": "AMPGAN v2", "maker": "uvm", "family": "uvm/ampgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10241, "dossier_id": "uvm/ampgan/ampgan-v3", "name": "AMPGAN v3", "maker": "uvm", "family": "uvm/ampgan", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10242, "dossier_id": "uvm/geodtr/geodtr-plus", "name": "GeoDTR+", "maker": "uvm", "family": "uvm/geodtr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10243, "dossier_id": "uvm/gpg2a/gpg2a", "name": "GPG2A", "maker": "uvm", "family": "uvm/gpg2a", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10244, "dossier_id": "uvm/hedonometer/hedonometer", "name": "Hedonometer", "maker": "uvm", "family": "uvm/hedonometer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10245, "dossier_id": "uvm/ousiometer/ousiometer", "name": "Ousiometer", "maker": "uvm", "family": "uvm/ousiometer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10246, "dossier_id": "uw/gapril/gapril-w-exp", "name": "gAPRIL with explanations", "maker": "uw", "family": "uw/gapril", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10247, "dossier_id": "uw/gapril/gapril-wo-exp", "name": "gAPRIL without explanations", "maker": "uw", "family": "uw/gapril", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10248, "dossier_id": "uw/guanaco/guanaco-13b", "name": "Guanaco 13B", "maker": "uw", "family": "uw/guanaco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10249, "dossier_id": "uw/guanaco/guanaco-33b", "name": "Guanaco 33B", "maker": "uw", "family": "uw/guanaco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10250, "dossier_id": "uw/guanaco/guanaco-65b", "name": "Guanaco 65B", "maker": "uw", "family": "uw/guanaco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10251, "dossier_id": "uw/guanaco/guanaco-7b", "name": "Guanaco 7B", "maker": "uw", "family": "uw/guanaco", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10252, "dossier_id": "uw/mathleap/mathleap-octen-8b", "name": "MathLeap-Octen-8B", "maker": "uw", "family": "uw/mathleap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10253, "dossier_id": "uw/mathleap/mathleap-qwen-8b", "name": "MathLeap-Qwen-8B", "maker": "uw", "family": "uw/mathleap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10254, "dossier_id": "uw/proteinmpnn/ligandmpnn", "name": "LigandMPNN", "maker": "uw", "family": "uw/proteinmpnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10255, "dossier_id": "uw/proteinmpnn/proteinmpnn", "name": "ProteinMPNN", "maker": "uw", "family": "uw/proteinmpnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10256, "dossier_id": "uw/proteinmpnn/solublempnn", "name": "SolubleMPNN", "maker": "uw", "family": "uw/proteinmpnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10257, "dossier_id": "uw/rfdiffusion/rfdiffusion", "name": "RFdiffusion", "maker": "uw", "family": "uw/rfdiffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10258, "dossier_id": "uw/rfdiffusion/rfdiffusion-all-atom", "name": "RFdiffusion All-Atom", "maker": "uw", "family": "uw/rfdiffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10259, "dossier_id": "uw/rfdiffusion/rfdiffusion2", "name": "RFdiffusion2", "maker": "uw", "family": "uw/rfdiffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10260, "dossier_id": "uw/rfdiffusion/rfdiffusion3", "name": "RFdiffusion3", "maker": "uw", "family": "uw/rfdiffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10261, "dossier_id": "uw/rfdiffusion/rfdiffusion3-na", "name": "RFdiffusion3NA", "maker": "uw", "family": "uw/rfdiffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10262, "dossier_id": "uw/rosettafold/rosettafold", "name": "RoseTTAFold", "maker": "uw", "family": "uw/rosettafold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10263, "dossier_id": "uw/rosettafold/rosettafold-all-atom", "name": "RoseTTAFold All-Atom", "maker": "uw", "family": "uw/rosettafold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10264, "dossier_id": "uw/rosettafold/rosettafold-na", "name": "RoseTTAFoldNA", "maker": "uw", "family": "uw/rosettafold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10265, "dossier_id": "uw/rosettafold/rosettafold2", "name": "RoseTTAFold2", "maker": "uw", "family": "uw/rosettafold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10266, "dossier_id": "uw/rosettafold/rosettafold3", "name": "RoseTTAFold3", "maker": "uw", "family": "uw/rosettafold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10267, "dossier_id": "uw/x-elm/x-elm", "name": "X-ELM", "maker": "uw", "family": "uw/x-elm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10268, "dossier_id": "uw/yolo/yolov1", "name": "YOLOv1", "maker": "uw", "family": "uw/yolo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10269, "dossier_id": "uw/yolo/yolov2", "name": "YOLOv2", "maker": "uw", "family": "uw/yolo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Real-time object detection. The maintained page treats YOLOv2 608 and Tiny YOLO as the v2 weight files.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10270, "dossier_id": "uw/yolo/yolov3", "name": "YOLOv3", "maker": "uw", "family": "uw/yolo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10271, "dossier_id": "uw-madison/llava/llava-1-5-13b", "name": "LLaVA-1.5 13B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10272, "dossier_id": "uw-madison/llava/llava-1-5-7b", "name": "LLaVA-1.5 7B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10273, "dossier_id": "uw-madison/llava/llava-13b", "name": "LLaVA 13B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10274, "dossier_id": "uw-madison/llava/llava-7b", "name": "LLaVA 7B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10275, "dossier_id": "uw-madison/llava/llava-llama-2-13b", "name": "LLaVA LLaMA-2 13B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10276, "dossier_id": "uw-madison/llava/llava-llama-2-7b", "name": "LLaVA LLaMA-2 7B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10277, "dossier_id": "uw-madison/llava/llava-mpt-7b", "name": "LLaVA-MPT 7B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10278, "dossier_id": "uw-madison/llava/llava-next-34b", "name": "LLaVA-NeXT 34B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10279, "dossier_id": "uw-madison/llava/llava-next-mistral-7b", "name": "LLaVA-NeXT Mistral 7B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10280, "dossier_id": "uw-madison/llava/llava-next-vicuna-13b", "name": "LLaVA-NeXT Vicuna 13B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10281, "dossier_id": "uw-madison/llava/llava-next-vicuna-7b", "name": "LLaVA-NeXT Vicuna 7B", "maker": "uw-madison", "family": "uw-madison/llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10282, "dossier_id": "uw-madison/seem/seem-focal-l", "name": "SEEM Focal-L", "maker": "uw-madison", "family": "uw-madison/seem", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10283, "dossier_id": "uw-madison/seem/seem-focal-t", "name": "SEEM Focal-T", "maker": "uw-madison", "family": "uw-madison/seem", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10284, "dossier_id": "uw-madison/vip-llava/vip-llava-13b", "name": "ViP-LLaVA 13B", "maker": "uw-madison", "family": "uw-madison/vip-llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10285, "dossier_id": "uw-madison/vip-llava/vip-llava-7b", "name": "ViP-LLaVA 7B", "maker": "uw-madison", "family": "uw-madison/vip-llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10286, "dossier_id": "uw-madison/vrsight/vrsight", "name": "VRSight", "maker": "uw-madison", "family": "uw-madison/vrsight", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10287, "dossier_id": "uw-madison/x-decoder/x-decoder-focal-l", "name": "X-Decoder Focal-L", "maker": "uw-madison", "family": "uw-madison/x-decoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10288, "dossier_id": "uw-madison/x-decoder/x-decoder-focal-t", "name": "X-Decoder Focal-T", "maker": "uw-madison", "family": "uw-madison/x-decoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10289, "dossier_id": "uzh/detect/detect", "name": "DETECT", "maker": "uzh", "family": "uzh/detect", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10290, "dossier_id": "uzh/romansh-nllb/romansh-nllb-1-3b", "name": "Romansh NLLB 1.3B", "maker": "uzh", "family": "uzh/romansh-nllb", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10291, "dossier_id": "uzh/simcse/unsup-simcse-xlm-roberta-base", "name": "Unsupervised SimCSE XLM-RoBERTa base", "maker": "uzh", "family": "uzh/simcse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10292, "dossier_id": "uzh/swiss-german/swiss-german-canine", "name": "Swiss German CANINE", "maker": "uzh", "family": "uzh/swiss-german", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10293, "dossier_id": "uzh/swiss-german/swiss-german-xlm-roberta-base", "name": "Swiss German XLM-RoBERTa base", "maker": "uzh", "family": "uzh/swiss-german", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10294, "dossier_id": "uzh/swissbert/swiss-german-swissbert-char", "name": "SwissBERT Swiss German character adapter", "maker": "uzh", "family": "uzh/swissbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10295, "dossier_id": "uzh/swissbert/swissbert-ner", "name": "SwissBERT NER", "maker": "uzh", "family": "uzh/swissbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10296, "dossier_id": "valencia/chris/chris-proba1", "name": "CHRIS-PROBA1", "maker": "valencia", "family": "valencia/chris", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10297, "dossier_id": "valencia/cloudsen12/cloudsen12", "name": "CloudSEN12", "maker": "valencia", "family": "valencia/cloudsen12", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10298, "dossier_id": "valencia/cloudsen12/cloudsen12-l2a", "name": "CloudSEN12 L2A", "maker": "valencia", "family": "valencia/cloudsen12", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card names this checkpoint as the model trained on 12 Sentinel-2 L2A bands of the CloudSEN12 dataset.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10299, "dossier_id": "valencia/cloudsen12/dtacs4bands", "name": "DTACS 4-band", "maker": "valencia", "family": "valencia/cloudsen12", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10300, "dossier_id": "valencia/cloudsen12/landsat30", "name": "CloudSEN12 Landsat 30 m", "maker": "valencia", "family": "valencia/cloudsen12", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10301, "dossier_id": "valencia/cloudsen12/unetmobv2-v1", "name": "UNetMobV2 V1", "maker": "valencia", "family": "valencia/cloudsen12", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10302, "dossier_id": "valencia/cloudsen12/unetmobv2-v2", "name": "UNetMobV2 V2", "maker": "valencia", "family": "valencia/cloudsen12", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The ISP card says UNetMobV2 V2 was trained on the 13 Sentinel-2 L1C bands of CloudSEN12+.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10303, "dossier_id": "valencia/fdr4vgt/fdr4vgt-cloud", "name": "FDR4VGT-CLOUD", "maker": "valencia", "family": "valencia/fdr4vgt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10304, "dossier_id": "valencia/hyperstarcop/hyperstarcop-mag1c", "name": "HyperSTARCOP mag1c", "maker": "valencia", "family": "valencia/hyperstarcop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10305, "dossier_id": "valencia/hyperstarcop/hyperstarcop-mag1c-rgb", "name": "HyperSTARCOP mag1c+RGB", "maker": "valencia", "family": "valencia/hyperstarcop", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10306, "dossier_id": "valencia/ldsr/ldsr-s2", "name": "LDSR-S2", "maker": "valencia", "family": "valencia/ldsr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10307, "dossier_id": "valencia/ml4floods/unet-multioutput", "name": "Unet multioutput", "maker": "valencia", "family": "valencia/ml4floods", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10308, "dossier_id": "valencia/ml4floods/unet-multioutput-rgbnir", "name": "Unet multioutput RGBNIR", "maker": "valencia", "family": "valencia/ml4floods", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10309, "dossier_id": "valencia/ml4floods/unet-multioutput-s2-l8", "name": "Unet multioutput S2-to-L8", "maker": "valencia", "family": "valencia/ml4floods", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10310, "dossier_id": "valencia/qa4eo/mss-cloudmask", "name": "MSS Cloud Detection Model (UNet-EfficientNet-B3)", "maker": "valencia", "family": "valencia/qa4eo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10311, "dossier_id": "valencia/sen2sr/sen2sr", "name": "SEN2SR", "maker": "valencia", "family": "valencia/sen2sr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10312, "dossier_id": "valencia/sen2sr/sen2srlite", "name": "SEN2SRLite", "maker": "valencia", "family": "valencia/sen2sr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10313, "dossier_id": "valencia/udl4fl/mm-unet-s1s2", "name": "mm_unet_s1s2", "maker": "valencia", "family": "valencia/udl4fl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10314, "dossier_id": "valencia/udl4fl/sm-unet-s1", "name": "sm_unet_s1", "maker": "valencia", "family": "valencia/udl4fl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-modality U-Net trained on Sentinel-1 GRD VV and VH from S1S2Water and Kuro Siwo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10315, "dossier_id": "valencia/udl4fl/sm-unet-s2", "name": "sm_unet_s2", "maker": "valencia", "family": "valencia/udl4fl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10316, "dossier_id": "vanderbilt/3d-ux-net/3d-ux-net", "name": "3D UX-Net", "maker": "vanderbilt", "family": "vanderbilt/3d-ux-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10317, "dossier_id": "vanderbilt/slant/slant", "name": "SLANT", "maker": "vanderbilt", "family": "vanderbilt/slant", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10318, "dossier_id": "vanderbilt/slant/slant-ticv", "name": "SLANT-TICV", "maker": "vanderbilt", "family": "vanderbilt/slant", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10319, "dossier_id": "vanderbilt/synb0/synb0-disco", "name": "Synb0-DISCO", "maker": "vanderbilt", "family": "vanderbilt/synb0", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10320, "dossier_id": "vanderbilt/synbold/synbold-disco", "name": "SynBOLD-DisCo", "maker": "vanderbilt", "family": "vanderbilt/synbold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10321, "dossier_id": "vanderbilt/unest/unest-renal", "name": "Renal UNesT", "maker": "vanderbilt", "family": "vanderbilt/unest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10322, "dossier_id": "vanderbilt/unest/unest-whole-brain", "name": "Whole-brain UNesT", "maker": "vanderbilt", "family": "vanderbilt/unest", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10323, "dossier_id": "veed/avatars/avatars", "name": "VEED Avatars", "maker": "veed", "family": "veed/avatars", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10324, "dossier_id": "veed/background-removal/video-background-removal", "name": "VEED Video Background Removal", "maker": "veed", "family": "veed/background-removal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10325, "dossier_id": "veed/clean-audio/clean-audio", "name": "VEED Clean Audio", "maker": "veed", "family": "veed/clean-audio", "context_length": null, "prompt_price_per_m": 0.0125, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10326, "dossier_id": "veed/dubbing/dubbing", "name": "VEED Dubbing", "maker": "veed", "family": "veed/dubbing", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10327, "dossier_id": "veed/fabric/fabric-1-0", "name": "VEED Fabric 1.0", "maker": "veed", "family": "veed/fabric", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10328, "dossier_id": "veed/lipsync/lipsync", "name": "VEED Lipsync", "maker": "veed", "family": "veed/lipsync", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.4, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10329, "dossier_id": "veed/lipsync/lipsync-2-0", "name": "VEED Lip Sync 2.0", "maker": "veed", "family": "veed/lipsync", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.07, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10330, "dossier_id": "veed/live-avatar/live-avatar", "name": "VEED Live Avatar", "maker": "veed", "family": "veed/live-avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10331, "dossier_id": "veed/subtitles/subtitles", "name": "VEED Subtitles", "maker": "veed", "family": "veed/subtitles", "context_length": null, "prompt_price_per_m": 0.1, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10332, "dossier_id": "vienna/awesom/awesom", "name": "aweSOM", "maker": "vienna", "family": "vienna/awesom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10333, "dossier_id": "vienna/fame/fame3r", "name": "FAME3R", "maker": "vienna", "family": "vienna/fame", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10334, "dossier_id": "vienna/griphin/griphin", "name": "GRIPHIN", "maker": "vienna", "family": "vienna/griphin", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10335, "dossier_id": "vienna/neat/neat", "name": "NEAT", "maker": "vienna", "family": "vienna/neat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10336, "dossier_id": "vienna/neat/neat-pocket", "name": "NEAT-POCKET", "maker": "vienna", "family": "vienna/neat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10337, "dossier_id": "vienna/trialblazer/trialblazer", "name": "Trialblazer", "maker": "vienna", "family": "vienna/trialblazer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10338, "dossier_id": "viggle/animate/viggle-animate", "name": "Viggle-Animate", "maker": "viggle", "family": "viggle/animate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.01, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10339, "dossier_id": "viggle/jst/jst-1", "name": "JST-1", "maker": "viggle", "family": "viggle/jst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10340, "dossier_id": "viggle/jst/jst-2", "name": "JST-2", "maker": "viggle", "family": "viggle/jst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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\".", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10341, "dossier_id": "viggle/meridian/meridian", "name": "Meridian", "maker": "viggle", "family": "viggle/meridian", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10342, "dossier_id": "viggle/viggle/viggle-2-0", "name": "Viggle 2.0.0", "maker": "viggle", "family": "viggle/viggle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10343, "dossier_id": "viggle/viggle/viggle-v3", "name": "Viggle V3", "maker": "viggle", "family": "viggle/viggle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.01, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10344, "dossier_id": "viggle/viggle/viggle-v4", "name": "Viggle V4", "maker": "viggle", "family": "viggle/viggle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.01, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10345, "dossier_id": "viggle/viggle/viggle-v4-5", "name": "Viggle V4.5", "maker": "viggle", "family": "viggle/viggle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10346, "dossier_id": "virginia/chrome/attentivechrome", "name": "AttentiveChrome", "maker": "virginia", "family": "virginia/chrome", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10347, "dossier_id": "visla/avatar/advanced-avatar", "name": "Advanced Avatar", "maker": "visla", "family": "visla/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10348, "dossier_id": "visla/avatar/basic-avatar", "name": "Basic Avatar", "maker": "visla", "family": "visla/avatar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10349, "dossier_id": "vivo/bluelm/bluelm-130b", "name": "\u84dd\u5fc3\u5927\u6a21\u578b130B", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10350, "dossier_id": "vivo/bluelm/bluelm-13b", "name": "BlueLM-13B", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10351, "dossier_id": "vivo/bluelm/bluelm-175b", "name": "\u84dd\u5fc3\u5927\u6a21\u578b175B", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Announced as the top knowledge-capacity model in the 2023 matrix. A separate product behavior was not documented.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10352, "dossier_id": "vivo/bluelm/bluelm-1b", "name": "\u84dd\u5fc3\u5927\u6a21\u578b1B", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10353, "dossier_id": "vivo/bluelm/bluelm-30b-moe", "name": "\u84dd\u5fc330B MoE\u7aef\u4fa7\u5927\u6a21\u578b", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10354, "dossier_id": "vivo/bluelm/bluelm-3b", "name": "\u84dd\u5fc3\u7aef\u4fa7\u5927\u6a21\u578b3B", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10355, "dossier_id": "vivo/bluelm/bluelm-70b", "name": "\u84dd\u5fc3\u5927\u6a21\u578b70B", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10356, "dossier_id": "vivo/bluelm/bluelm-70b-2", "name": "BlueLM-70B 2.0", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10357, "dossier_id": "vivo/bluelm/bluelm-flash", "name": "BlueLM-Flash", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10358, "dossier_id": "vivo/bluelm/bluelm-pro", "name": "BlueLM-Pro", "maker": "vivo", "family": "vivo/bluelm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10359, "dossier_id": "vivo/bluelm-art/bluelm-art", "name": "BlueLM-Art", "maker": "vivo", "family": "vivo/bluelm-art", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10360, "dossier_id": "vivo/bluelm-gui/bluelm-gui", "name": "BlueLM-GUI", "maker": "vivo", "family": "vivo/bluelm-gui", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10361, "dossier_id": "vivo/bluelm-speech/bluelm-realtime", "name": "BlueLM-RealTime", "maker": "vivo", "family": "vivo/bluelm-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10362, "dossier_id": "vivo/bluelm-speech/bluelm-speech", "name": "\u84dd\u5fc3\u8bed\u97f3\u5927\u6a21\u578b", "maker": "vivo", "family": "vivo/bluelm-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10363, "dossier_id": "vivo/bluelm-speech/bluelm-speech-2", "name": "\u84dd\u5fc3\u8bed\u97f3\u5927\u6a21\u578b2.0", "maker": "vivo", "family": "vivo/bluelm-speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10364, "dossier_id": "vivo/bluelm-v/bluelm-7b-vl", "name": "BlueLM-7B-VL", "maker": "vivo", "family": "vivo/bluelm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Announced as a future open multimodal model. No task list was published.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10365, "dossier_id": "vivo/bluelm-v/bluelm-nano", "name": "BlueLM-Nano", "maker": "vivo", "family": "vivo/bluelm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10366, "dossier_id": "vivo/bluelm-v/bluelm-v-3b", "name": "BlueLM-V-3B", "maker": "vivo", "family": "vivo/bluelm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10367, "dossier_id": "voyage/voyage-healthcare/voyage-healthcare-2", "name": "voyage-healthcare-2", "maker": "voyage", "family": "voyage/voyage-healthcare", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10368, "dossier_id": "vt/haven/haven", "name": "HAVEN", "maker": "vt", "family": "vt/haven", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10369, "dossier_id": "vt/lakefm/lakefm-5m", "name": "LakeFM 5M", "maker": "vt", "family": "vt/lakefm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10370, "dossier_id": "vt/lakefm/lakefm-7m", "name": "LakeFM 7M", "maker": "vt", "family": "vt/lakefm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10371, "dossier_id": "vt/phylo-diffusion/phylo-diffusion", "name": "Phylo-Diffusion", "maker": "vt", "family": "vt/phylo-diffusion", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10372, "dossier_id": "wageningen/deepcgm/deepcgm", "name": "DeepCGM", "maker": "wageningen", "family": "wageningen/deepcgm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10373, "dossier_id": "warsaw/hydramp/hydramp", "name": "HydrAMP", "maker": "warsaw", "family": "warsaw/hydramp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10374, "dossier_id": "warsaw/hyformer/hyformer-peptides-34m", "name": "Hyformer peptides 34M", "maker": "warsaw", "family": "warsaw/hyformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10375, "dossier_id": "warsaw/hyformer/hyformer-peptides-34m-mic", "name": "Hyformer peptides 34M MIC", "maker": "warsaw", "family": "warsaw/hyformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10376, "dossier_id": "warsaw/omegamp/omegamp", "name": "OmegAMP", "maker": "warsaw", "family": "warsaw/omegamp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10377, "dossier_id": "warwick/cerberus/cerberus", "name": "Cerberus", "maker": "warwick", "family": "warwick/cerberus", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10378, "dossier_id": "warwick/hovernet/hover-net", "name": "HoVer-Net", "maker": "warwick", "family": "warwick/hovernet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10379, "dossier_id": "warwick/hovernet/hovernet-plus", "name": "HoVer-Net+", "maker": "warwick", "family": "warwick/hovernet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10380, "dossier_id": "warwick/kongnet/kongnet", "name": "KongNet", "maker": "warwick", "family": "warwick/kongnet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10381, "dossier_id": "warwick/mapde/mapde", "name": "MapDe", "maker": "warwick", "family": "warwick/mapde", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The toolbox citation calls it a deconvolving convolutional network for cell detection. Both catalogued checkpoints share one input size.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10382, "dossier_id": "warwick/micronet/micronet", "name": "Micro-Net", "maker": "warwick", "family": "warwick/micronet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10383, "dossier_id": "warwick/nuclick/nuclick", "name": "NuClick", "maker": "warwick", "family": "warwick/nuclick", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10384, "dossier_id": "warwick/patch-classifier/alexnet-kather100k", "name": "AlexNet Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10385, "dossier_id": "warwick/patch-classifier/alexnet-pcam", "name": "AlexNet PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10386, "dossier_id": "warwick/patch-classifier/densenet121-kather100k", "name": "DenseNet-121 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10387, "dossier_id": "warwick/patch-classifier/densenet121-pcam", "name": "DenseNet-121 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10388, "dossier_id": "warwick/patch-classifier/densenet161-kather100k", "name": "DenseNet-161 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10389, "dossier_id": "warwick/patch-classifier/densenet161-pcam", "name": "DenseNet-161 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10390, "dossier_id": "warwick/patch-classifier/densenet169-kather100k", "name": "DenseNet-169 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10391, "dossier_id": "warwick/patch-classifier/densenet169-pcam", "name": "DenseNet-169 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10392, "dossier_id": "warwick/patch-classifier/densenet201-kather100k", "name": "DenseNet-201 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10393, "dossier_id": "warwick/patch-classifier/densenet201-pcam", "name": "DenseNet-201 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10394, "dossier_id": "warwick/patch-classifier/googlenet-kather100k", "name": "GoogLeNet Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10395, "dossier_id": "warwick/patch-classifier/googlenet-pcam", "name": "GoogLeNet PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10396, "dossier_id": "warwick/patch-classifier/mobilenet-v2-kather100k", "name": "MobileNetV2 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10397, "dossier_id": "warwick/patch-classifier/mobilenet-v2-pcam", "name": "MobileNetV2 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10398, "dossier_id": "warwick/patch-classifier/mobilenet-v3-large-kather100k", "name": "MobileNetV3-Large Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10399, "dossier_id": "warwick/patch-classifier/mobilenet-v3-large-pcam", "name": "MobileNetV3-Large PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10400, "dossier_id": "warwick/patch-classifier/mobilenet-v3-small-kather100k", "name": "MobileNetV3-Small Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10401, "dossier_id": "warwick/patch-classifier/mobilenet-v3-small-pcam", "name": "MobileNetV3-Small PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10402, "dossier_id": "warwick/patch-classifier/resnet101-kather100k", "name": "ResNet-101 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10403, "dossier_id": "warwick/patch-classifier/resnet101-pcam", "name": "ResNet-101 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10404, "dossier_id": "warwick/patch-classifier/resnet18-kather100k", "name": "ResNet-18 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10405, "dossier_id": "warwick/patch-classifier/resnet18-pcam", "name": "ResNet-18 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10406, "dossier_id": "warwick/patch-classifier/resnet34-kather100k", "name": "ResNet-34 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10407, "dossier_id": "warwick/patch-classifier/resnet34-pcam", "name": "ResNet-34 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10408, "dossier_id": "warwick/patch-classifier/resnet50-kather100k", "name": "ResNet-50 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10409, "dossier_id": "warwick/patch-classifier/resnet50-pcam", "name": "ResNet-50 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10410, "dossier_id": "warwick/patch-classifier/wide-resnet101-2-kather100k", "name": "Wide ResNet-101-2 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10411, "dossier_id": "warwick/patch-classifier/wide-resnet101-2-pcam", "name": "Wide ResNet-101-2 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10412, "dossier_id": "warwick/patch-classifier/wide-resnet50-2-kather100k", "name": "Wide ResNet-50-2 Kather100k", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10413, "dossier_id": "warwick/patch-classifier/wide-resnet50-2-pcam", "name": "Wide ResNet-50-2 PCam", "maker": "warwick", "family": "warwick/patch-classifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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`.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10414, "dossier_id": "warwick/sccnn/sccnn", "name": "SCCNN", "maker": "warwick", "family": "warwick/sccnn", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10415, "dossier_id": "warwick/segformer/segformer-b5-distilled", "name": "SegFormer-B5 distilled from Virchow2", "maker": "warwick", "family": "warwick/segformer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10416, "dossier_id": "warwick/semantic-segmentor/fcn-resnet50-unet-bcss", "name": "FCN-ResNet50-UNet BCSS", "maker": "warwick", "family": "warwick/semantic-segmentor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The pretrained page says it was trained on the BCSS dataset for semantic segmentation of breast cancer histology.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10417, "dossier_id": "warwick/semantic-segmentor/fcn-tissue-mask", "name": "FCN tissue mask", "maker": "warwick", "family": "warwick/semantic-segmentor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The pretrained page says it masks tissue in whole-slide images. It was trained on internal TIA Centre data.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10418, "dossier_id": "waseda/comet/comet-t5-base-japanese", "name": "COMET-T5 ja", "maker": "waseda", "family": "waseda/comet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10419, "dossier_id": "waseda/roberta/roberta-jtruthfulqa", "name": "Waseda RoBERTa JTruthfulQA", "maker": "waseda", "family": "waseda/roberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10420, "dossier_id": "waterloo/abc/abc", "name": "ABC", "maker": "waterloo", "family": "waterloo/abc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10421, "dossier_id": "waterloo/acecoder/acecoder", "name": "AceCoder", "maker": "waterloo", "family": "waterloo/acecoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10422, "dossier_id": "waterloo/acecoder/acecoderm", "name": "AceCodeRM", "maker": "waterloo", "family": "waterloo/acecoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It scores code pairs for the AceCoder training setup. It is not the generator.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10423, "dossier_id": "waterloo/afriberta/afriberta", "name": "AfriBERTa", "maker": "waterloo", "family": "waterloo/afriberta", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10424, "dossier_id": "waterloo/afriberta-v2/afriberta-v2", "name": "AfriBERTa v2", "maker": "waterloo", "family": "waterloo/afriberta-v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10425, "dossier_id": "waterloo/afriteva/afriteva", "name": "AfriTeVa", "maker": "waterloo", "family": "waterloo/afriteva", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multilingual generation and fine-tuning for the same language set as the 2021 AfriBERTa models. It is not AfriTeVa v2.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10426, "dossier_id": "waterloo/afriteva-v2/afriteva-v2", "name": "AfriTeVa v2", "maker": "waterloo", "family": "waterloo/afriteva-v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10427, "dossier_id": "waterloo/aggretriever/aggretriever", "name": "Aggretriever", "maker": "waterloo", "family": "waterloo/aggretriever", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10428, "dossier_id": "waterloo/azbert/azbert", "name": "azbert-base", "maker": "waterloo", "family": "waterloo/azbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fill-mask. No usage note was retrieved.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10429, "dossier_id": "waterloo/browseragent/browseragent", "name": "BrowserAgent", "maker": "waterloo", "family": "waterloo/browseragent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10430, "dossier_id": "waterloo/canard/t5-canard", "name": "T5-base CANARD", "maker": "waterloo", "family": "waterloo/canard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10431, "dossier_id": "waterloo/cft/cft", "name": "Critique Fine-Tuning", "maker": "waterloo", "family": "waterloo/cft", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10432, "dossier_id": "waterloo/consisti2v/consisti2v", "name": "ConsistI2V", "maker": "waterloo", "family": "waterloo/consisti2v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10433, "dossier_id": "waterloo/cosdpr/cosdpr", "name": "cosDPR", "maker": "waterloo", "family": "waterloo/cosdpr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10434, "dossier_id": "waterloo/critique-coder/critique-coder", "name": "Critique-Coder", "maker": "waterloo", "family": "waterloo/critique-coder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10435, "dossier_id": "waterloo/doc2query/doc2query-t5", "name": "doc2query-T5", "maker": "waterloo", "family": "waterloo/doc2query", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10436, "dossier_id": "waterloo/duot5/duot5", "name": "DuoT5", "maker": "waterloo", "family": "waterloo/duot5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10437, "dossier_id": "waterloo/editreward/editreward", "name": "EditReward", "maker": "waterloo", "family": "waterloo/editreward", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10438, "dossier_id": "waterloo/fim/fim", "name": "FIM", "maker": "waterloo", "family": "waterloo/fim", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10439, "dossier_id": "waterloo/first/first", "name": "FIRST", "maker": "waterloo", "family": "waterloo/first", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10440, "dossier_id": "waterloo/gaggle/gaggle", "name": "Gaggle", "maker": "waterloo", "family": "waterloo/gaggle", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10441, "dossier_id": "waterloo/general-reasoner/general-reasoner", "name": "General-Reasoner", "maker": "waterloo", "family": "waterloo/general-reasoner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10442, "dossier_id": "waterloo/general-verifier/general-verifier", "name": "General-Verifier", "maker": "waterloo", "family": "waterloo/general-verifier", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10443, "dossier_id": "waterloo/lit5/lit5-distill", "name": "LiT5-Distill", "maker": "waterloo", "family": "waterloo/lit5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10444, "dossier_id": "waterloo/lit5/lit5-score", "name": "LiT5-Score", "maker": "waterloo", "family": "waterloo/lit5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pointwise or score-style reranking distilled into T5, as the companion to LiT5-Distill. The card bodies were not read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10445, "dossier_id": "waterloo/mammoth/mammoth", "name": "MAmmoTH", "maker": "waterloo", "family": "waterloo/mammoth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10446, "dossier_id": "waterloo/mammoth-vl/mammoth-vl2", "name": "MAmmoTH-VL2", "maker": "waterloo", "family": "waterloo/mammoth-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10447, "dossier_id": "waterloo/mammoth2/mammoth2", "name": "MAmmoTH2", "maker": "waterloo", "family": "waterloo/mammoth2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10448, "dossier_id": "waterloo/mantis/mantis", "name": "Mantis", "maker": "waterloo", "family": "waterloo/mantis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10449, "dossier_id": "waterloo/mdpr/mdpr", "name": "mDPR", "maker": "waterloo", "family": "waterloo/mdpr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10450, "dossier_id": "waterloo/monobert/monobert", "name": "MonoBERT", "maker": "waterloo", "family": "waterloo/monobert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pointwise cross-encoder reranking of MS MARCO passages. The finetune-only repo is a training variant of the same product.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10451, "dossier_id": "waterloo/monoelectra/monoelectra", "name": "monoELECTRA", "maker": "waterloo", "family": "waterloo/monoelectra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text ranking with an ELECTRA cross-encoder. Training data and scores were not on the listing.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10452, "dossier_id": "waterloo/monot5/monot5", "name": "MonoT5", "maker": "waterloo", "family": "waterloo/monot5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10453, "dossier_id": "waterloo/one-shot-cft/one-shot-cft", "name": "One-Shot CFT", "maker": "waterloo", "family": "waterloo/one-shot-cft", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10454, "dossier_id": "waterloo/pixelreasoner/pixelreasoner", "name": "PixelReasoner", "maker": "waterloo", "family": "waterloo/pixelreasoner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10455, "dossier_id": "waterloo/rankllama/rankllama", "name": "RankLLaMA", "maker": "waterloo", "family": "waterloo/rankllama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10456, "dossier_id": "waterloo/rankvicuna/rankvicuna", "name": "RankVicuna", "maker": "waterloo", "family": "waterloo/rankvicuna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10457, "dossier_id": "waterloo/rankzephyr/rankzephyr", "name": "RankZephyr", "maker": "waterloo", "family": "waterloo/rankzephyr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10458, "dossier_id": "waterloo/rational-rewards/rational-rewards", "name": "RationalRewards", "maker": "waterloo", "family": "waterloo/rational-rewards", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10459, "dossier_id": "waterloo/repllama/repllama", "name": "RepLLaMA", "maker": "waterloo", "family": "waterloo/repllama", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10460, "dossier_id": "waterloo/scholarcopilot/scholarcopilot", "name": "ScholarCopilot", "maker": "waterloo", "family": "waterloo/scholarcopilot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10461, "dossier_id": "waterloo/slimr/slimr", "name": "SLIM", "maker": "waterloo", "family": "waterloo/slimr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10462, "dossier_id": "waterloo/structlm/structlm", "name": "StructLM", "maker": "waterloo", "family": "waterloo/structlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10463, "dossier_id": "waterloo/swe-next/swe-next", "name": "SWE-Next", "maker": "waterloo", "family": "waterloo/swe-next", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10464, "dossier_id": "waterloo/tct-colbert/tct-colbert", "name": "TCT-ColBERT", "maker": "waterloo", "family": "waterloo/tct-colbert", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10465, "dossier_id": "waterloo/tigerscore/tigerscore", "name": "TIGERScore", "maker": "waterloo", "family": "waterloo/tigerscore", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10466, "dossier_id": "waterloo/unicoil/unicoil", "name": "uniCOIL", "maker": "waterloo", "family": "waterloo/unicoil", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10467, "dossier_id": "waterloo/unir/unir", "name": "UniIR", "maker": "waterloo", "family": "waterloo/unir", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10468, "dossier_id": "waterloo/vamba/vamba", "name": "Vamba", "maker": "waterloo", "family": "waterloo/vamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10469, "dossier_id": "waterloo/videoscore/videoscore", "name": "VideoScore", "maker": "waterloo", "family": "waterloo/videoscore", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10470, "dossier_id": "waterloo/videoscore/videoscore2", "name": "VideoScore2", "maker": "waterloo", "family": "waterloo/videoscore", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10471, "dossier_id": "waterloo/viscoder/viscoder", "name": "VisCoder", "maker": "waterloo", "family": "waterloo/viscoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The models generate code for visual tasks from the VisCode-200K set. VisCoder2 is the later multilingual line.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10472, "dossier_id": "waterloo/viscoder/viscoder2", "name": "VisCoder2", "maker": "waterloo", "family": "waterloo/viscoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10473, "dossier_id": "waterloo/vista/vista", "name": "VISTA", "maker": "waterloo", "family": "waterloo/vista", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10474, "dossier_id": "waterloo/vl-reasoner/vl-reasoner", "name": "VL-Reasoner", "maker": "waterloo", "family": "waterloo/vl-reasoner", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The lab publishes these as visual question-answering models under the VL-Reasoner name, separate from the VL-Rethinker repos.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10475, "dossier_id": "waterloo/vl-rethinker/vl-rethinker", "name": "VL-Rethinker", "maker": "waterloo", "family": "waterloo/vl-rethinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10476, "dossier_id": "waterloo/vlm2vec/vlm2vec", "name": "VLM2Vec", "maker": "waterloo", "family": "waterloo/vlm2vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10477, "dossier_id": "waterloo/vlm2vec/vlm2vec-v2", "name": "VLM2Vec-V2", "maker": "waterloo", "family": "waterloo/vlm2vec", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10478, "dossier_id": "weizmann/brain-encoder/universal-brain-encoder", "name": "Universal Brain Encoder", "maker": "weizmann", "family": "weizmann/brain-encoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10479, "dossier_id": "weizmann/brain-it/brain-it", "name": "Brain-IT", "maker": "weizmann", "family": "weizmann/brain-it", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10480, "dossier_id": "weizmann/spec2class/spec2class", "name": "Spec2Class", "maker": "weizmann", "family": "weizmann/spec2class", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10481, "dossier_id": "wellsaid/caruso/caruso", "name": "Caruso", "maker": "wellsaid", "family": "wellsaid/caruso", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10482, "dossier_id": "wellsaid/global/global", "name": "Global", "maker": "wellsaid", "family": "wellsaid/global", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10483, "dossier_id": "wellsaid/legacy/legacy", "name": "Legacy (Standard)", "maker": "wellsaid", "family": "wellsaid/legacy", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10484, "dossier_id": "wellsaid/preview/preview-13-2", "name": "Preview 13.2", "maker": "wellsaid", "family": "wellsaid/preview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10485, "dossier_id": "western/clar/clar", "name": "CLAR", "maker": "western", "family": "western/clar", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10486, "dossier_id": "western/hippunfold/hippunfold", "name": "HippUnfold", "maker": "western", "family": "western/hippunfold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10487, "dossier_id": "westlake/cryo-ief/cryo-ief", "name": "Cryo-IEF", "maker": "westlake", "family": "westlake/cryo-ief", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10488, "dossier_id": "westlake/cryoverse/cryoverse", "name": "cryoVERSE", "maker": "westlake", "family": "westlake/cryoverse", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10489, "dossier_id": "westlake/cycleresearcher/cycleresearcher-123b", "name": "CycleResearcher 123B", "maker": "westlake", "family": "westlake/cycleresearcher", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same ML paper-generation series. On the lab's reviewer table it scores below the 12B and 72B models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10490, "dossier_id": "westlake/cycleresearcher/cycleresearcher-12b", "name": "CycleResearcher 12B", "maker": "westlake", "family": "westlake/cycleresearcher", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10491, "dossier_id": "westlake/cycleresearcher/cycleresearcher-72b", "name": "CycleResearcher 72B", "maker": "westlake", "family": "westlake/cycleresearcher", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10492, "dossier_id": "westlake/cyclereviewer/cyclereviewer-123b", "name": "CycleReviewer 123B", "maker": "westlake", "family": "westlake/cyclereviewer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10493, "dossier_id": "westlake/cyclereviewer/cyclereviewer-70b", "name": "CycleReviewer 70B", "maker": "westlake", "family": "westlake/cyclereviewer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10494, "dossier_id": "westlake/cyclereviewer/cyclereviewer-8b", "name": "CycleReviewer 8B", "maker": "westlake", "family": "westlake/cyclereviewer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10495, "dossier_id": "westlake/deepreviewer/deepreviewer-14b", "name": "DeepReviewer 14B", "maker": "westlake", "family": "westlake/deepreviewer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10496, "dossier_id": "westlake/deepreviewer/deepreviewer-7b", "name": "DeepReviewer 7B", "maker": "westlake", "family": "westlake/deepreviewer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10497, "dossier_id": "westlake/domino/domino", "name": "DOMINO", "maker": "westlake", "family": "westlake/domino", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/ .", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10498, "dossier_id": "westlake/e3-cryofold/e3-cryofold", "name": "E3-CryoFold", "maker": "westlake", "family": "westlake/e3-cryofold", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10499, "dossier_id": "westlake/evolla/evolla-10b", "name": "Evolla 10B", "maker": "westlake", "family": "westlake/evolla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10500, "dossier_id": "westlake/evolla/evolla-80b", "name": "Evolla 80B", "maker": "westlake", "family": "westlake/evolla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10501, "dossier_id": "westlake/leanvae/leanvae", "name": "LeanVAE", "maker": "westlake", "family": "westlake/leanvae", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10502, "dossier_id": "westlake/pinal/pinal", "name": "Pinal", "maker": "westlake", "family": "westlake/pinal", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/ .", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10503, "dossier_id": "westlake/protrek/protrek-35m", "name": "ProTrek 35M", "maker": "westlake", "family": "westlake/protrek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10504, "dossier_id": "westlake/protrek/protrek-650m", "name": "ProTrek 650M", "maker": "westlake", "family": "westlake/protrek", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10505, "dossier_id": "westlake/saprot/saprot-1-3b", "name": "SaProt 1.3B", "maker": "westlake", "family": "westlake/saprot", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10506, "dossier_id": "whu/hypersigma/hypersigma-base", "name": "HyperSIGMA base", "maker": "whu", "family": "whu/hypersigma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10507, "dossier_id": "whu/hypersigma/hypersigma-huge", "name": "HyperSIGMA huge", "maker": "whu", "family": "whu/hypersigma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10508, "dossier_id": "whu/hypersigma/hypersigma-large", "name": "HyperSIGMA large", "maker": "whu", "family": "whu/hypersigma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10509, "dossier_id": "whu/mars/mars-base", "name": "MaRS base", "maker": "whu", "family": "whu/mars", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10510, "dossier_id": "whu/mars/mars-large", "name": "MaRS large", "maker": "whu", "family": "whu/mars", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10511, "dossier_id": "whu/rsp/rsp-resnet-50", "name": "RSP-ResNet-50", "maker": "whu", "family": "whu/rsp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10512, "dossier_id": "whu/rsp/rsp-swin-t", "name": "RSP-Swin-T", "maker": "whu", "family": "whu/rsp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10513, "dossier_id": "whu/rsp/rsp-vitaev2-s", "name": "RSP-ViTAEv2-S", "maker": "whu", "family": "whu/rsp", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10514, "dossier_id": "wonder-dynamics/mocap/ai-mocap", "name": "AI Motion Capture", "maker": "wonder-dynamics", "family": "wonder-dynamics/mocap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10515, "dossier_id": "wonder-dynamics/neural-layer/neural-layer", "name": "Neural Layer", "maker": "wonder-dynamics", "family": "wonder-dynamics/neural-layer", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10516, "dossier_id": "wonder-dynamics/rigging/ai-rigging", "name": "AI Rigging", "maker": "wonder-dynamics", "family": "wonder-dynamics/rigging", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10517, "dossier_id": "wonder-dynamics/wonder-3d/wonder-3d", "name": "Wonder 3D", "maker": "wonder-dynamics", "family": "wonder-dynamics/wonder-3d", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10518, "dossier_id": "wonder-dynamics/wonder-animation/wonder-animation", "name": "Wonder Animation", "maker": "wonder-dynamics", "family": "wonder-dynamics/wonder-animation", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10519, "dossier_id": "wonder-dynamics/wonder-tools/camera-track", "name": "Camera Track", "maker": "wonder-dynamics", "family": "wonder-dynamics/wonder-tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10520, "dossier_id": "wonder-dynamics/wonder-tools/clean-plate", "name": "Clean Plate", "maker": "wonder-dynamics", "family": "wonder-dynamics/wonder-tools", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10521, "dossier_id": "world-labs/atlas/atlas", "name": "Atlas", "maker": "world-labs", "family": "world-labs/atlas", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10522, "dossier_id": "world-labs/marble/marble-1-0", "name": "Marble 1.0", "maker": "world-labs", "family": "world-labs/marble", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10523, "dossier_id": "world-labs/marble/marble-1-0-draft", "name": "Marble 1.0 Draft", "maker": "world-labs", "family": "world-labs/marble", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10524, "dossier_id": "world-labs/marble/marble-1-1", "name": "Marble 1.1", "maker": "world-labs", "family": "world-labs/marble", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10525, "dossier_id": "world-labs/marble/marble-1-1-plus", "name": "Marble 1.1 Plus", "maker": "world-labs", "family": "world-labs/marble", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10526, "dossier_id": "world-labs/rtfm/rtfm", "name": "RTFM", "maker": "world-labs", "family": "world-labs/rtfm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10527, "dossier_id": "wpi/hybriddepth/hybriddepth", "name": "HybridDepth", "maker": "wpi", "family": "wpi/hybriddepth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10528, "dossier_id": "writer/camel/camel-5b", "name": "Camel-5B", "maker": "writer", "family": "writer/camel", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10529, "dossier_id": "writer/palmyra/instructpalmyra-20b", "name": "InstructPalmyra-20b", "maker": "writer", "family": "writer/palmyra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10530, "dossier_id": "writer/palmyra/palmyra-20b-chat", "name": "Palmyra-20b-chat", "maker": "writer", "family": "writer/palmyra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "A chat-tuned Palmyra 20B release. Writer's card says the model is no longer maintained or supported.", "summary_intro": "A chat-tuned Palmyra 20B release. Writer's card says the model is no longer maintained or supported. Collected 2026-10-15.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10531, "dossier_id": "writer/palmyra/palmyra-56b-instruct", "name": "Palmyra-56B-Instruct", "maker": "writer", "family": "writer/palmyra", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10532, "dossier_id": "writer/palmyra-fin/palmyra-fin", "name": "Palmyra Fin", "maker": "writer", "family": "writer/palmyra-fin", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 12.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10533, "dossier_id": "writer/palmyra-fin/palmyra-fin-32k", "name": "Palmyra Fin 32k", "maker": "writer", "family": "writer/palmyra-fin", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 12.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10534, "dossier_id": "writer/palmyra-med/palmyra-med", "name": "Palmyra Med", "maker": "writer", "family": "writer/palmyra-med", "context_length": null, "prompt_price_per_m": 5.0, "completion_price_per_m": 12.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10535, "dossier_id": "writer/palmyra-med/palmyra-med-20b", "name": "Palmyra-Med-20b", "maker": "writer", "family": "writer/palmyra-med", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10536, "dossier_id": "writer/palmyra-vision/palmyra-vision", "name": "Palmyra Vision", "maker": "writer", "family": "writer/palmyra-vision", "context_length": null, "prompt_price_per_m": 7.5, "completion_price_per_m": 22.5, "status": "deprecated", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10537, "dossier_id": "writer/palmyra-x/palmyra-x-002-32k", "name": "Palmyra X 002 32k", "maker": "writer", "family": "writer/palmyra-x", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": 2.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10538, "dossier_id": "writer/palmyra-x/palmyra-x-002-instruct", "name": "Palmyra X 002 Instruct", "maker": "writer", "family": "writer/palmyra-x", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": 2.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10539, "dossier_id": "writer/palmyra-x/palmyra-x-003-instruct", "name": "Palmyra X 003 Instruct", "maker": "writer", "family": "writer/palmyra-x", "context_length": null, "prompt_price_per_m": 7.5, "completion_price_per_m": 22.5, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10540, "dossier_id": "writer/palmyra-x/palmyra-x-32k-instruct", "name": "Palmyra X 32k Instruct", "maker": "writer", "family": "writer/palmyra-x", "context_length": null, "prompt_price_per_m": 1.0, "completion_price_per_m": 2.0, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10541, "dossier_id": "writer/palmyra-x/palmyra-x-4-3-73b", "name": "Palmyra-X-4.3-73B", "maker": "writer", "family": "writer/palmyra-x", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10542, "dossier_id": "wsu/deep-cwd-ihc/deep-cwd-ihc", "name": "Deep-CWD-IHC", "maker": "wsu", "family": "wsu/deep-cwd-ihc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10543, "dossier_id": "wsu/foreswe/foreswe", "name": "ForeSWE", "maker": "wsu", "family": "wsu/foreswe", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10544, "dossier_id": "wuerzburg/llammlein/betzerl-1b", "name": "Betzerl 1B", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10545, "dossier_id": "wuerzburg/llammlein/llammlein-1b-chat", "name": "LL\u00e4Mmlein 1B Chat", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10546, "dossier_id": "wuerzburg/llammlein/llammlein-7b-chat", "name": "LL\u00e4Mmlein 7B Chat", "maker": "wuerzburg", "family": "wuerzburg/llammlein", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10547, "dossier_id": "wustl/birdsat/birdsat", "name": "BirdSAT", "maker": "wustl", "family": "wustl/birdsat", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10548, "dossier_id": "wustl/context-cir/context-cir", "name": "ConText-CIR", "maker": "wustl", "family": "wustl/context-cir", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10549, "dossier_id": "wustl/genesis/genesis", "name": "Genesis", "maker": "wustl", "family": "wustl/genesis", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10550, "dossier_id": "wustl/geoclap/geoclap", "name": "GeoCLAP", "maker": "wustl", "family": "wustl/geoclap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10551, "dossier_id": "wustl/geosynth/geosynth", "name": "GeoSynth", "maker": "wustl", "family": "wustl/geosynth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10552, "dossier_id": "wustl/prom3e/prom3e", "name": "ProM3E", "maker": "wustl", "family": "wustl/prom3e", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10553, "dossier_id": "wustl/quari/quari", "name": "QuARI", "maker": "wustl", "family": "wustl/quari", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10554, "dossier_id": "wustl/range/range", "name": "RANGE", "maker": "wustl", "family": "wustl/range", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10555, "dossier_id": "wustl/rcme/rcme", "name": "RCME", "maker": "wustl", "family": "wustl/rcme", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10556, "dossier_id": "wustl/sat2cap/sat2cap", "name": "Sat2Cap", "maker": "wustl", "family": "wustl/sat2cap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10557, "dossier_id": "wustl/sat2sound/sat2sound", "name": "Sat2Sound", "maker": "wustl", "family": "wustl/sat2sound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10558, "dossier_id": "wustl/taxabind/taxabind", "name": "TaxaBind", "maker": "wustl", "family": "wustl/taxabind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10559, "dossier_id": "wustl/terradit/terradit", "name": "TerraDiT", "maker": "wustl", "family": "wustl/terradit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10560, "dossier_id": "wustl/terradit/terradit-omega", "name": "TerraDiT-Omega", "maker": "wustl", "family": "wustl/terradit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10561, "dossier_id": "wustl/trajloc/trajloc", "name": "TrajLoc", "maker": "wustl", "family": "wustl/trajloc", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10562, "dossier_id": "wustl/tte/tte", "name": "TTE", "maker": "wustl", "family": "wustl/tte", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10563, "dossier_id": "wustl/vectorsynth/vectorsynth", "name": "VectorSynth", "maker": "wustl", "family": "wustl/vectorsynth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10564, "dossier_id": "wvu/fedfound/fedfound", "name": "FedFound", "maker": "wvu", "family": "wvu/fedfound", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10565, "dossier_id": "xai/grok/grok-1-5v", "name": "Grok-1.5V", "maker": "xai", "family": "xai/grok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10566, "dossier_id": "xai/grok/grok-2-mini", "name": "Grok-2 mini", "maker": "xai", "family": "xai/grok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10567, "dossier_id": "xai/grok/grok-2-vision", "name": "Grok-2 Vision", "maker": "xai", "family": "xai/grok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10568, "dossier_id": "xai/grok/grok-3-mini", "name": "Grok 3 mini", "maker": "xai", "family": "xai/grok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10569, "dossier_id": "xai/grok/grok-4-1", "name": "Grok 4.1", "maker": "xai", "family": "xai/grok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10570, "dossier_id": "xai/grok/grok-4-heavy", "name": "Grok 4 Heavy", "maker": "xai", "family": "xai/grok", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10571, "dossier_id": "xai/grok/grok-code-fast-1", "name": "Grok Code Fast 1", "maker": "xai", "family": "xai/grok", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": 1.5, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10572, "dossier_id": "xai/imagine/aurora", "name": "Aurora", "maker": "xai", "family": "xai/imagine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10573, "dossier_id": "xai/imagine/grok-imagine-image", "name": "Grok Imagine Image", "maker": "xai", "family": "xai/imagine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.02, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10574, "dossier_id": "xai/imagine/grok-imagine-image-2-0", "name": "Grok Imagine Image 2.0", "maker": "xai", "family": "xai/imagine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.04, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10575, "dossier_id": "xai/imagine/grok-imagine-image-pro", "name": "Grok Imagine Image Pro", "maker": "xai", "family": "xai/imagine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10576, "dossier_id": "xai/imagine/grok-imagine-image-quality", "name": "Grok Imagine Image Quality", "maker": "xai", "family": "xai/imagine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10577, "dossier_id": "xai/imagine/grok-imagine-video", "name": "Grok Imagine Video", "maker": "xai", "family": "xai/imagine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.05, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10578, "dossier_id": "xai/imagine/grok-imagine-video-1-5", "name": "Grok Imagine Video 1.5", "maker": "xai", "family": "xai/imagine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.08, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10579, "dossier_id": "xai/imagine/grok-imagine-video-1-5-lite", "name": "Grok Imagine Video 1.5 Lite", "maker": "xai", "family": "xai/imagine", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.02, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10580, "dossier_id": "xai/voice/grok-voice-1-0", "name": "Grok Voice 1.0", "maker": "xai", "family": "xai/voice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10581, "dossier_id": "xai/voice/grok-voice-2-0", "name": "Grok Voice 2.0", "maker": "xai", "family": "xai/voice", "context_length": null, "prompt_price_per_m": 0.004, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10582, "dossier_id": "xai/voice/grok-voice-3-0", "name": "Grok Voice 3.0", "maker": "xai", "family": "xai/voice", "context_length": null, "prompt_price_per_m": 0.004, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10583, "dossier_id": "xai/voice/grok-voice-transcribe-1-0", "name": "Grok Voice Transcribe 1.0", "maker": "xai", "family": "xai/voice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10584, "dossier_id": "xai/voice/grok-voice-transcribe-2-0", "name": "Grok Voice Transcribe 2.0", "maker": "xai", "family": "xai/voice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10585, "dossier_id": "xai/voice/text-to-speech", "name": "Grok Text to Speech", "maker": "xai", "family": "xai/voice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 15.0, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10586, "dossier_id": "xiaomi/midasheng/midashenglm-0-6b", "name": "MiDashengLM-0.6B", "maker": "xiaomi", "family": "xiaomi/midasheng", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi offers it as a lighter audio-captioning model when the 7B model is too heavy.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10587, "dossier_id": "xiaomi/midasheng/midashenglm-7b", "name": "MiDashengLM-7B", "maker": "xiaomi", "family": "xiaomi/midasheng", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi built it for general audio understanding in phones, homes, and cars, not for cloning a voice.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10588, "dossier_id": "xiaomi/milm/milm-1-3b", "name": "MiLM-1.3B", "maker": "xiaomi", "family": "xiaomi/milm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10589, "dossier_id": "xiaomi/milm/milm-6b", "name": "MiLM-6B", "maker": "xiaomi", "family": "xiaomi/milm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10590, "dossier_id": "xiaomi/milm/milm2-0-7b-x8", "name": "MiLM2-0.7B\u00d78", "maker": "xiaomi", "family": "xiaomi/milm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi described the MoE pair as a way to keep device-side latency down while mixing specialized experts.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10591, "dossier_id": "xiaomi/milm/milm2-1-3b", "name": "MiLM2-1.3B", "maker": "xiaomi", "family": "xiaomi/milm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device assistant tasks inside Xiaomi's device ecosystem.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10592, "dossier_id": "xiaomi/milm/milm2-2b-x8", "name": "MiLM2-2B\u00d78", "maker": "xiaomi", "family": "xiaomi/milm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "An efficiency model for Xiaomi's own device and cloud mix.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10593, "dossier_id": "xiaomi/milm/milm2-6b", "name": "MiLM2-6B", "maker": "xiaomi", "family": "xiaomi/milm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10594, "dossier_id": "xiaomi/mimo/mimo-7b-base", "name": "MiMo-7B-Base", "maker": "xiaomi", "family": "xiaomi/mimo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10595, "dossier_id": "xiaomi/mimo/mimo-7b-rl", "name": "MiMo-7B-RL", "maker": "xiaomi", "family": "xiaomi/mimo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10596, "dossier_id": "xiaomi/mimo/mimo-7b-rl-zero", "name": "MiMo-7B-RL-Zero", "maker": "xiaomi", "family": "xiaomi/mimo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10597, "dossier_id": "xiaomi/mimo/mimo-7b-sft", "name": "MiMo-7B-SFT", "maker": "xiaomi", "family": "xiaomi/mimo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi uses it as the cold start before reinforcement learning on verifiable math and code problems.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10598, "dossier_id": "xiaomi/mimo-audio/mimo-audio-7b-base", "name": "MiMo-Audio-7B-Base", "maker": "xiaomi", "family": "xiaomi/mimo-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi presents it as a few-shot audio foundation, not only an ASR or TTS model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10599, "dossier_id": "xiaomi/mimo-audio/mimo-audio-7b-instruct", "name": "MiMo-Audio-7B-Instruct", "maker": "xiaomi", "family": "xiaomi/mimo-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10600, "dossier_id": "xiaomi/mimo-audio/mimo-audio-tokenizer", "name": "MiMo-Audio-Tokenizer", "maker": "xiaomi", "family": "xiaomi/mimo-audio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "It turns audio into RVQ tokens for MiMo-Audio and is also the tokenizer for MiMo-V2.5-ASR.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10601, "dossier_id": "xiaomi/mimo-embodied/mimo-embodied-7b", "name": "MiMo-Embodied-7B", "maker": "xiaomi", "family": "xiaomi/mimo-embodied", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi says it covers task planning, affordance, spatial understanding, driving perception, status prediction, and driving planning.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10602, "dossier_id": "xiaomi/mimo-v2/mimo-v2-tts", "name": "MiMo-V2-TTS", "maker": "xiaomi", "family": "xiaomi/mimo-v2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi described it as the voice for agents, including emotion, mid-sentence tone changes, singing, and regional Chinese dialects.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10603, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5-asr", "name": "MiMo-V2.5-ASR", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": null, "prompt_price_per_m": 0.5, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi says it is the speech-input side of the agent stack: meetings, noisy audio, dialects, and song lyrics.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10604, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5-pro-ultraspeed", "name": "MiMo-V2.5-Pro-UltraSpeed", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi offered it for users who wanted roughly 10x generation speed at 3x the Pro price.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10605, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5-tts", "name": "MiMo-V2.5-TTS", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10606, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5-tts-voiceclone", "name": "MiMo-V2.5-TTS-VoiceClone", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi says a small audio sample clones a target voice and still follows style instructions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10607, "dossier_id": "xiaomi/mimo-v2-5/mimo-v2-5-tts-voicedesign", "name": "MiMo-V2.5-TTS-VoiceDesign", "maker": "xiaomi", "family": "xiaomi/mimo-v2-5", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi says one sentence can define a new timbre.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10608, "dossier_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", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi says it covers coding, general agents, visual coding, and cybersecurity, and is a base for open agent RL research.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10609, "dossier_id": "xiaomi/mimo-vl/mimo-vl-7b-rl", "name": "MiMo-VL-7B-RL", "maker": "xiaomi", "family": "xiaomi/mimo-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10610, "dossier_id": "xiaomi/mimo-vl/mimo-vl-7b-sft", "name": "MiMo-VL-7B-SFT", "maker": "xiaomi", "family": "xiaomi/mimo-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Xiaomi says the SFT stage is the general visual-understanding checkpoint before mixed on-policy RL.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10611, "dossier_id": "xiaomi/mimo-vl/mimo-vl-miloco-7b", "name": "MiMo-VL-Miloco-7B", "maker": "xiaomi", "family": "xiaomi/mimo-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10612, "dossier_id": "xidian/cryptollm/cryptollm", "name": "\u7384\u77e5\u5927\u6a21\u578b", "maker": "xidian", "family": "xidian/cryptollm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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/.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10613, "dossier_id": "xidian/huitong/huitong", "name": "\u6167\u901a\u5927\u6a21\u578b", "maker": "xidian", "family": "xidian/huitong", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10614, "dossier_id": "xidian/industrial-vision/industrial-vision", "name": "\u5de5\u4e1a\u667a\u80fd\u89c6\u89c9\u8bc6\u522b\u5927\u6a21\u578b", "maker": "xidian", "family": "xidian/industrial-vision", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10615, "dossier_id": "xidian/qinling/qinling-rs-brain", "name": "\u79e6\u5cad\u00b7\u897f\u7535\u9065\u611f\u8111", "maker": "xidian", "family": "xidian/qinling", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10616, "dossier_id": "xidian/yilv/yilv", "name": "\u4e1d\u8def\u9038\u65c5", "maker": "xidian", "family": "xidian/yilv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10617, "dossier_id": "xjtu/alignearth/alignearth-sar-vit-b-16", "name": "AlignEarth-SAR-ViT-B-16", "maker": "xjtu", "family": "xjtu/alignearth", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10618, "dossier_id": "xjtu/ban/ban", "name": "BAN", "maker": "xjtu", "family": "xjtu/ban", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10619, "dossier_id": "xjtu/fast-lewm/fast-lewm", "name": "Fast-LeWM", "maker": "xjtu", "family": "xjtu/fast-lewm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10620, "dossier_id": "xjtu/power-llava/power-llava", "name": "Power-LLaVA", "maker": "xjtu", "family": "xjtu/power-llava", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10621, "dossier_id": "yale/autoacu/a2cu-generator", "name": "A2CU Generator", "maker": "yale", "family": "yale/autoacu", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10622, "dossier_id": "yale/brio/brio-cnndm-cased", "name": "BRIO CNN/DailyMail Cased", "maker": "yale", "family": "yale/brio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10623, "dossier_id": "yale/brio/brio-cnndm-uncased", "name": "BRIO CNN/DailyMail Uncased", "maker": "yale", "family": "yale/brio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10624, "dossier_id": "yale/brio/brio-xsum-cased", "name": "BRIO XSum Cased", "maker": "yale", "family": "yale/brio", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10625, "dossier_id": "yale/mdcure/mdcure-flant5-base", "name": "MDCure-FlanT5-Base", "maker": "yale", "family": "yale/mdcure", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10626, "dossier_id": "yale/mdcure/mdcure-flant5-large", "name": "MDCure-FlanT5-Large", "maker": "yale", "family": "yale/mdcure", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Same job as the other MDCure generators: follow an instruction over several documents. The Hub task tag is summarization.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10627, "dossier_id": "yale/mdcure/mdcure-llama3-1-70b-instruct", "name": "MDCure-Llama-3.1-70B-Instruct", "maker": "yale", "family": "yale/mdcure", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10628, "dossier_id": "yale/mdcure/mdcure-llama3-1-8b-instruct", "name": "MDCure-Llama-3.1-8B-Instruct", "maker": "yale", "family": "yale/mdcure", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10629, "dossier_id": "yale/mdcure/mdcure-qwen2-1-5b-instruct", "name": "MDCure-Qwen2-1.5B-Instruct", "maker": "yale", "family": "yale/mdcure", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10630, "dossier_id": "yale/mdcure/mdcure-qwen2-7b-instruct", "name": "MDCure-Qwen2-7B-Instruct", "maker": "yale", "family": "yale/mdcure", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Multi-document instruction following. The suite table calls it the best Qwen2-7B-Instruct run on MDCure-72k.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10631, "dossier_id": "yale/mdcure/mdcure-rm", "name": "MDCureRM", "maker": "yale", "family": "yale/mdcure", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10632, "dossier_id": "yale/reastap/reastap-large", "name": "ReasTAP-Large", "maker": "yale", "family": "yale/reastap", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10633, "dossier_id": "yandex/alice/alice-ai-art", "name": "Alice AI ART", "maker": "yandex", "family": "yandex/alice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Create or edit pictures from text, including logos and captions, in Alice and, as an API model, in AI Studio.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10634, "dossier_id": "yandex/alice/alice-ai-llm-search", "name": "Alice AI LLM Search", "maker": "yandex", "family": "yandex/alice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10635, "dossier_id": "yandex/alice/alice-ai-omni", "name": "Alice AI Omni", "maker": "yandex", "family": "yandex/alice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10636, "dossier_id": "yandex/alice/alice-ai-search", "name": "Alice AI Search", "maker": "yandex", "family": "yandex/alice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Short grounded answers in Yandex Search. Yandex says the tune used user signals from real search sessions.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10637, "dossier_id": "yandex/alice/alice-ai-vlm", "name": "Alice AI VLM", "maker": "yandex", "family": "yandex/alice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10638, "dossier_id": "yandex/alice/aliceai-foundation-80b", "name": "AliceAI-Foundation-80B-A3B-Base", "maker": "yandex", "family": "yandex/alice", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10639, "dossier_id": "yandex/embeddings/text-embeddings", "name": "Yandex Text Embeddings 1", "maker": "yandex", "family": "yandex/embeddings", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fine-tune embeddings when the stock document and query models are not enough.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10640, "dossier_id": "yandex/embeddings/text-embeddings-doc", "name": "Yandex Text Embeddings Doc", "maker": "yandex", "family": "yandex/embeddings", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed long texts such as documentation so they can be compared with query embeddings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10641, "dossier_id": "yandex/embeddings/text-embeddings-query", "name": "Yandex Text Embeddings Query", "maker": "yandex", "family": "yandex/embeddings", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed search queries and short requests into the same space as document embeddings.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10642, "dossier_id": "yandex/embeddings/text-embeddings-v2-doc", "name": "Yandex Text Embeddings v2 Doc", "maker": "yandex", "family": "yandex/embeddings", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed long source texts at a chosen dimension.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10643, "dossier_id": "yandex/embeddings/text-embeddings-v2-query", "name": "Yandex Text Embeddings v2 Query", "maker": "yandex", "family": "yandex/embeddings", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embed short queries for search against v2 document vectors.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10644, "dossier_id": "yandex/sona/sona", "name": "Sona", "maker": "yandex", "family": "yandex/sona", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Pick and rank music from listening history, skips, and likes, without a separate candidate-generation stack.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10645, "dossier_id": "yandex/speech/speech-realtime", "name": "Speech Realtime", "maker": "yandex", "family": "yandex/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Low-latency voice agents. The gallery calls it the specialized model for voice scenarios.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10646, "dossier_id": "yandex/speech/speech-tts-live", "name": "Speech TTS Live", "maker": "yandex", "family": "yandex/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Stream speech from text. Not a language model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10647, "dossier_id": "yandex/speech/speechkit-general", "name": "SpeechKit General", "maker": "yandex", "family": "yandex/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Streaming and file speech recognition across the languages SpeechKit lists for this model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10648, "dossier_id": "yandex/yandexart/yandexart", "name": "YandexART", "maker": "yandex", "family": "yandex/yandexart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image and short animation in Shedevrum, Alice, Yandex ads, and the Cloud image API.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10649, "dossier_id": "yandex/yandexart/yandexart-1-3", "name": "YandexART 1.3", "maker": "yandex", "family": "yandex/yandexart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Text-to-image generation before the hybrid 2.0 architecture.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10650, "dossier_id": "yandex/yandexart/yandexart-2-0", "name": "YandexART 2.0", "maker": "yandex", "family": "yandex/yandexart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Logos, banners, and ad images in Cloud, Alice Pro, and Yandex services.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10651, "dossier_id": "yandex/yandexart/yandexart-2-5", "name": "YandexART 2.5", "maker": "yandex", "family": "yandex/yandexart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10652, "dossier_id": "yandex/yandexart/yandexart-2-5-pro", "name": "YandexART 2.5 Pro", "maker": "yandex", "family": "yandex/yandexart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Higher-detail image generation inside Shedevrum for paying subscribers.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10653, "dossier_id": "yandex/yandexgpt/yandexgpt", "name": "YandexGPT", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10654, "dossier_id": "yandex/yandexgpt/yandexgpt-2", "name": "YandexGPT 2", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10655, "dossier_id": "yandex/yandexgpt/yandexgpt-2-lite", "name": "YandexGPT 2 Lite", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.2, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Fast text tasks in YandexGPT API before YandexGPT 3 Lite became the default Lite model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10656, "dossier_id": "yandex/yandexgpt/yandexgpt-3-lite", "name": "YandexGPT 3 Lite", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": null, "prompt_price_per_m": 0.2, "completion_price_per_m": 0.2, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Chatbots, spelling checks, and data analysis where response speed matters more than the Pro model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10657, "dossier_id": "yandex/yandexgpt/yandexgpt-3-pro", "name": "YandexGPT 3 Pro", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": null, "prompt_price_per_m": 1.2, "completion_price_per_m": 1.2, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10658, "dossier_id": "yandex/yandexgpt/yandexgpt-4-lite", "name": "YandexGPT 4 Lite", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Faster, simpler text tasks. Yandex said its quality matched the previous generation's strongest model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10659, "dossier_id": "yandex/yandexgpt/yandexgpt-pro-32k", "name": "YandexGPT Pro 32k", "maker": "yandex", "family": "yandex/yandexgpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10660, "dossier_id": "yonsei/ai-ecg-af/ai-ecg-af", "name": "AI-ECG-AF", "maker": "yonsei", "family": "yonsei/ai-ecg-af", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10661, "dossier_id": "yonsei/deepcrispr/deepbe", "name": "DeepBE", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10662, "dossier_id": "yonsei/deepcrispr/deepcas9variants", "name": "DeepCas9variants", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10663, "dossier_id": "yonsei/deepcrispr/deepcpf1", "name": "DeepCpf1", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10664, "dossier_id": "yonsei/deepcrispr/deeppe", "name": "DeepPE", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10665, "dossier_id": "yonsei/deepcrispr/deepprime", "name": "DeepPrime", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10666, "dossier_id": "yonsei/deepcrispr/deepspcas9", "name": "DeepSpCas9", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10667, "dossier_id": "yonsei/deepcrispr/deepspcas9-ng", "name": "DeepSpCas9-NG", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10668, "dossier_id": "yonsei/deepcrispr/deepspcas9variants", "name": "DeepSpCas9variants", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10669, "dossier_id": "yonsei/deepcrispr/deepxcas9", "name": "DeepxCas9", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10670, "dossier_id": "yonsei/deepcrispr/peselector", "name": "PEselector", "maker": "yonsei", "family": "yonsei/deepcrispr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10671, "dossier_id": "yonsei/preti/preti", "name": "PRETI", "maker": "yonsei", "family": "yonsei/preti", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10672, "dossier_id": "yonsei/tesla/tesla", "name": "TESLA", "maker": "yonsei", "family": "yonsei/tesla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10673, "dossier_id": "york/albedomm/albedomm", "name": "AlbedoMM", "maker": "york", "family": "york/albedomm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10674, "dossier_id": "york/inverserendernet/inverserendernet", "name": "InverseRenderNet", "maker": "york", "family": "york/inverserendernet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10675, "dossier_id": "york/inverserendernet/inverserendernet-plus-plus", "name": "InverseRenderNet++", "maker": "york", "family": "york/inverserendernet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10676, "dossier_id": "york/lyhm/lyhm", "name": "Liverpool-York Head Model (LYHM)", "maker": "york", "family": "york/lyhm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10677, "dossier_id": "york/neusky/neusky", "name": "NeuSky", "maker": "york", "family": "york/neusky", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10678, "dossier_id": "york/reni/reni", "name": "RENI", "maker": "york", "family": "york/reni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10679, "dossier_id": "york/reni/reni-plus-plus", "name": "RENI++", "maker": "york", "family": "york/reni", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10680, "dossier_id": "york/yem/yem", "name": "York Ear Model (YEM)", "maker": "york", "family": "york/yem", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10681, "dossier_id": "york/yolso/yolso", "name": "YOLSO", "maker": "york", "family": "york/yolso", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10682, "dossier_id": "yorku/chartgemma/chartgemma", "name": "ChartGemma", "maker": "yorku", "family": "yorku/chartgemma", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10683, "dossier_id": "yorku/chartinstruct/chartinstruct-flan-t5-xl", "name": "ChartInstruct-Flan-T5-XL", "maker": "yorku", "family": "yorku/chartinstruct", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10684, "dossier_id": "yorku/colflor/colflor", "name": "ColFlor", "maker": "yorku", "family": "yorku/colflor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10685, "dossier_id": "yorku/unichart/unichart", "name": "UniChart", "maker": "yorku", "family": "yorku/unichart", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10686, "dossier_id": "youdao/bce/bce-embedding-base-v1", "name": "bce-embedding-base_v1", "maker": "youdao", "family": "youdao/bce", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10687, "dossier_id": "youdao/bce/bce-reranker-base-v1", "name": "bce-reranker-base_v1", "maker": "youdao", "family": "youdao/bce", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10688, "dossier_id": "youdao/qanything/qwen-7b-qanything", "name": "Qwen-7B-QAnything", "maker": "youdao", "family": "youdao/qanything", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10689, "dossier_id": "youdao/speech/confucius4-r2t2", "name": "Confucius4-R2T2", "maker": "youdao", "family": "youdao/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10690, "dossier_id": "youdao/speech/confucius4-tts", "name": "Confucius4-TTS", "maker": "youdao", "family": "youdao/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10691, "dossier_id": "youdao/speech/emotivoice", "name": "\u6613\u9b54\u58f0 (EmotiVoice)", "maker": "youdao", "family": "youdao/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-source", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10692, "dossier_id": "youdao/speech/youdao-llm-tts", "name": "\u6709\u9053\u5927\u6a21\u578b\u8bed\u97f3\u590d\u523b", "maker": "youdao", "family": "youdao/speech", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10693, "dossier_id": "youdao/translate/confucius4-t3po", "name": "Confucius4-T3PO", "maker": "youdao", "family": "youdao/translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10694, "dossier_id": "youdao/translate/ynmt", "name": "YNMT", "maker": "youdao", "family": "youdao/translate", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10695, "dossier_id": "youdao/translate/ziyue-translate-lite", "name": "\u5b50\u66f0\u7ffb\u8bd1 Lite", "maker": "youdao", "family": "youdao/translate", "context_length": null, "prompt_price_per_m": 0.01, "completion_price_per_m": 0.01, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10696, "dossier_id": "youdao/translate/ziyue-translate-pro", "name": "\u5b50\u66f0\u7ffb\u8bd1 Pro", "maker": "youdao", "family": "youdao/translate", "context_length": null, "prompt_price_per_m": 0.03, "completion_price_per_m": 0.03, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10697, "dossier_id": "youdao/ziyue/confucius-o1-14b", "name": "\u5b50\u66f0-o1 (Confucius-o1-14B)", "maker": "youdao", "family": "youdao/ziyue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10698, "dossier_id": "youdao/ziyue/confucius3-math", "name": "\u5b50\u66f03\u6570\u5b66\u6a21\u578b (Confucius3-Math)", "maker": "youdao", "family": "youdao/ziyue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10699, "dossier_id": "youdao/ziyue/confucius4", "name": "\u5b50\u66f04 (Confucius4)", "maker": "youdao", "family": "youdao/ziyue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10700, "dossier_id": "youdao/ziyue/ziyue", "name": "\u5b50\u66f0", "maker": "youdao", "family": "youdao/ziyue", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10701, "dossier_id": "zaragoza/panoramic-blitznet/panoramic-blitznet", "name": "Panoramic BlitzNet", "maker": "zaragoza", "family": "zaragoza/panoramic-blitznet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10702, "dossier_id": "zaragoza/panoramic-cnn-360-saliency/panoramic-cnn-360-saliency", "name": "Panoramic CNN 360 Saliency", "maker": "zaragoza", "family": "zaragoza/panoramic-cnn-360-saliency", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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`.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10703, "dossier_id": "zaragoza/precisecam/precisecam", "name": "PreciseCam", "maker": "zaragoza", "family": "zaragoza/precisecam", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10704, "dossier_id": "zaragoza/scangan360/scangan360", "name": "ScanGAN360", "maker": "zaragoza", "family": "zaragoza/scangan360", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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).", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10705, "dossier_id": "zaragoza/tspm-net/tspm-net", "name": "tSPM-Net", "maker": "zaragoza", "family": "zaragoza/tspm-net", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10706, "dossier_id": "zhipu/charglm/charglm-3", "name": "CharGLM-3", "maker": "zhipu", "family": "zhipu/charglm", "context_length": null, "prompt_price_per_m": 0.015, "completion_price_per_m": 0.015, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CharGLM-3 is a charglm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10707, "dossier_id": "zhipu/codegeex/codegeex2-6b", "name": "CodeGeeX2-6B", "maker": "zhipu", "family": "zhipu/codegeex", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "CodeGeeX2 6B, the 2023 code model based on ChatGLM2. Hugging Face repo created 2023-07-19. An int4 pack exists.", "summary_intro": "CodeGeeX2-6B is a codegeex model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10708, "dossier_id": "zhipu/cogagent/cogagent", "name": "CogAgent", "maker": "zhipu", "family": "zhipu/cogagent", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogAgent is a cogagent model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10709, "dossier_id": "zhipu/cogvideo/cogvideo", "name": "CogVideo", "maker": "zhipu", "family": "zhipu/cogvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogVideo is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10710, "dossier_id": "zhipu/cogvideo/cogvideox-1-5-5b", "name": "CogVideoX1.5-5B", "maker": "zhipu", "family": "zhipu/cogvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10711, "dossier_id": "zhipu/cogvideo/cogvideox-2", "name": "CogVideoX-2", "maker": "zhipu", "family": "zhipu/cogvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogVideoX-2 is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10712, "dossier_id": "zhipu/cogvideo/cogvideox-2b", "name": "CogVideoX-2B", "maker": "zhipu", "family": "zhipu/cogvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller August 2024 open text-to-video model. Apache-2.0. Repo CogVideoX-2b created 2024-08-05.", "summary_intro": "CogVideoX-2B is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10713, "dossier_id": "zhipu/cogvideo/cogvideox-3", "name": "CogVideoX-3", "maker": "zhipu", "family": "zhipu/cogvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogVideoX-3 is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10714, "dossier_id": "zhipu/cogvideo/cogvideox-5b", "name": "CogVideoX-5B", "maker": "zhipu", "family": "zhipu/cogvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogVideoX-5B is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10715, "dossier_id": "zhipu/cogvideo/cogvideox-flash", "name": "CogVideoX-Flash", "maker": "zhipu", "family": "zhipu/cogvideo", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Free video API on the China price table. The older index calls it a free, easy video model.", "summary_intro": "CogVideoX-Flash is a cogvideo model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10716, "dossier_id": "zhipu/cogview/cogview-2", "name": "CogView2", "maker": "zhipu", "family": "zhipu/cogview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogView2 is a cogview model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10717, "dossier_id": "zhipu/cogview/cogview-3", "name": "CogView-3", "maker": "zhipu", "family": "zhipu/cogview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogView-3 is a cogview model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10718, "dossier_id": "zhipu/cogview/cogview-3-flash", "name": "CogView-3-Flash", "maker": "zhipu", "family": "zhipu/cogview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10719, "dossier_id": "zhipu/cogview/cogview-3-plus", "name": "CogView3-Plus-3B", "maker": "zhipu", "family": "zhipu/cogview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10720, "dossier_id": "zhipu/cogview/cogview-4", "name": "CogView-4", "maker": "zhipu", "family": "zhipu/cogview", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogView-4 is a cogview model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10721, "dossier_id": "zhipu/cogvlm/cogvlm", "name": "CogVLM", "maker": "zhipu", "family": "zhipu/cogvlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogVLM is a cogvlm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10722, "dossier_id": "zhipu/cogvlm/cogvlm2", "name": "CogVLM2", "maker": "zhipu", "family": "zhipu/cogvlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "CogVLM2 is a cogvlm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10723, "dossier_id": "zhipu/cogvlm/visualglm-6b", "name": "VisualGLM-6B", "maker": "zhipu", "family": "zhipu/cogvlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "VisualGLM-6B, the Chinese visual chat model based on ChatGLM, repo created 2023-05-17.", "summary_intro": "VisualGLM-6B is a cogvlm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10724, "dossier_id": "zhipu/glm/chatglm-6b", "name": "ChatGLM-6B", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ChatGLM-6B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10725, "dossier_id": "zhipu/glm/chatglm2-6b", "name": "ChatGLM2-6B", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Second ChatGLM generation, 6B, repo created 2023-06-24. A 32K variant and int4 packs exist.", "summary_intro": "ChatGLM2-6B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10726, "dossier_id": "zhipu/glm/chatglm3-6b", "name": "ChatGLM3-6B", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "ChatGLM3-6B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10727, "dossier_id": "zhipu/glm/glm-130b", "name": "GLM-130B", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "GLM-130B is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: medium.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10728, "dossier_id": "zhipu/glm/glm-3-turbo", "name": "GLM-3-Turbo", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10729, "dossier_id": "zhipu/glm/glm-4-0520", "name": "GLM-4-0520", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10730, "dossier_id": "zhipu/glm/glm-4-9b", "name": "GLM-4-9B", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10731, "dossier_id": "zhipu/glm/glm-4-9b-0414", "name": "GLM-4-9B-0414", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10732, "dossier_id": "zhipu/glm/glm-4-air", "name": "GLM-4-Air", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10733, "dossier_id": "zhipu/glm/glm-4-alltools", "name": "GLM-4-AllTools", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10734, "dossier_id": "zhipu/glm/glm-4-flash", "name": "GLM-4-Flash", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Original free GLM-4-Flash, before Flash-250414. Still on the fine-tune and private-instance lists.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10735, "dossier_id": "zhipu/glm/glm-4-voice", "name": "GLM-4-Voice", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": 80.0, "completion_price_per_m": 80.0, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10736, "dossier_id": "zhipu/glm/glm-asr", "name": "GLM-ASR", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "GLM-ASR is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: deprecated. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10737, "dossier_id": "zhipu/glm/glm-asr-2512", "name": "GLM-ASR-2512", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": 0.03, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10738, "dossier_id": "zhipu/glm/glm-asr-nano-2512", "name": "GLM-ASR-Nano-2512", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open automatic-speech-recognition checkpoint GLM-ASR-Nano-2512, MIT, Hugging Face created 2025-12-09. Tag glmasr.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10739, "dossier_id": "zhipu/glm/glm-edge-1-5b-chat", "name": "GLM-Edge-1.5B-Chat", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device GLM edge chat model, about 1.5B, created 2024-11-20. License tag other. A GGUF pack exists.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10740, "dossier_id": "zhipu/glm/glm-edge-4b-chat", "name": "GLM-Edge-4B-Chat", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device GLM edge chat model, about 4B, created 2024-11-20. License tag other.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10741, "dossier_id": "zhipu/glm/glm-realtime-air", "name": "GLM-Realtime-Air", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10742, "dossier_id": "zhipu/glm/glm-realtime-flash", "name": "GLM-Realtime-Flash", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10743, "dossier_id": "zhipu/glm/glm-tts", "name": "GLM-TTS", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "GLM-TTS is a glm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10744, "dossier_id": "zhipu/glm/glm-tts-clone", "name": "GLM-TTS-Clone", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10745, "dossier_id": "zhipu/glm/glm-z1-32b-0414", "name": "GLM-Z1-32B-0414", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10746, "dossier_id": "zhipu/glm/glm-z1-9b-0414", "name": "GLM-Z1-9B-0414", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 9B reasoning model, MIT, created 2025-04-08.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10747, "dossier_id": "zhipu/glm/glm-z1-rumination-32b-0414", "name": "GLM-Z1-Rumination-32B-0414", "maker": "zhipu", "family": "zhipu/glm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Open 32B model with a rumination, tool-using deep-research mode. MIT. Hugging Face created 2025-04-13.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10748, "dossier_id": "zhipu/glm-image/glm-image", "name": "GLM-Image", "maker": "zhipu", "family": "zhipu/glm-image", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10749, "dossier_id": "zhipu/glm-v/autoglm-phone", "name": "AutoGLM-Phone", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10750, "dossier_id": "zhipu/glm-v/glm-4-1v-9b-thinking", "name": "GLM-4.1V-9B-Thinking", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10751, "dossier_id": "zhipu/glm-v/glm-4v", "name": "GLM-4V", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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).", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10752, "dossier_id": "zhipu/glm-v/glm-4v-9b", "name": "GLM-4V-9B", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "June 2024 open 9B vision-language model, license tag other. Base for the December 2024 CogAgent-9B checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10753, "dossier_id": "zhipu/glm-v/glm-4v-plus", "name": "GLM-4V-Plus", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10754, "dossier_id": "zhipu/glm-v/glm-edge-v-2b", "name": "GLM-Edge-V-2B", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device vision edge model, about 2B, created 2024-11-24. License tag other.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10755, "dossier_id": "zhipu/glm-v/glm-edge-v-5b", "name": "GLM-Edge-V-5B", "maker": "zhipu", "family": "zhipu/glm-v", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "On-device vision edge model, about 5B, created 2024-11-24. License tag other.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10756, "dossier_id": "zhipu/scail/scail-2", "name": "SCAIL-2", "maker": "zhipu", "family": "zhipu/scail", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "summary_intro": "SCAIL-2 is a scail model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: active. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10757, "dossier_id": "zhipu/webglm/webglm", "name": "WebGLM", "maker": "zhipu", "family": "zhipu/webglm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "retired", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "WebGLM, the 2023 web-augmented GLM. Repo created 2023-05-28. A 2B variant exists. Paper arXiv:2306.07906.", "summary_intro": "WebGLM is a webglm model from Zhipu AI, also branded Z.ai. Catalog status on 2026-10-15: retired. Confidence: high.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10758, "dossier_id": "zju/chatcell/chatcell-base", "name": "ChatCell-base", "maker": "zju", "family": "zju/chatcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-cell analysis model in the ChatCell size series. The card body and paper were not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10759, "dossier_id": "zju/chatcell/chatcell-large", "name": "ChatCell-large", "maker": "zju", "family": "zju/chatcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-cell analysis model in the ChatCell size series. The card body and paper were not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10760, "dossier_id": "zju/chatcell/chatcell-small", "name": "ChatCell-small", "maker": "zju", "family": "zju/chatcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Single-cell analysis model in the ChatCell size series. The card body and paper were not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10761, "dossier_id": "zju/chineseguard/chineseguard-1-5b", "name": "ChineseGuard-1.5B", "maker": "zju", "family": "zju/chineseguard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10762, "dossier_id": "zju/chineseguard/chineseguard-3b", "name": "ChineseGuard-3B", "maker": "zju", "family": "zju/chineseguard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10763, "dossier_id": "zju/chineseguard/chineseguard-7b", "name": "ChineseGuard-7B", "maker": "zju", "family": "zju/chineseguard", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10764, "dossier_id": "zju/datamind/datamind-14b", "name": "DataMind-14B", "maker": "zju", "family": "zju/datamind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10765, "dossier_id": "zju/datamind/datamind-7b", "name": "DataMind-7B", "maker": "zju", "family": "zju/datamind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10766, "dossier_id": "zju/datamind/datamind-analysis-14b", "name": "DataMind-Analysis-Qwen2.5-14B", "maker": "zju", "family": "zju/datamind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Larger DataMind analysis checkpoint paired with the 7B analysis model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10767, "dossier_id": "zju/datamind/datamind-analysis-7b", "name": "DataMind-Analysis-Qwen2.5-7B", "maker": "zju", "family": "zju/datamind", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Analysis specialist tied to the DataMind paper. The Hub tags say text generation for data analysis and code.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10768, "dossier_id": "zju/datapr/datapr-dabstep", "name": "DataPRM-DABStep", "maker": "zju", "family": "zju/datapr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named DataPRM checkpoint for DABStep. The card was not opened, so the reward or scoring interface is not stated.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10769, "dossier_id": "zju/datapr/datapr-scienceagentbench", "name": "DataPRM-ScienceAgentBench", "maker": "zju", "family": "zju/datapr", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named DataPRM checkpoint for ScienceAgentBench. The card was not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10770, "dossier_id": "zju/haldet/haldet-13b", "name": "HalDet-LLaVA-13B", "maker": "zju", "family": "zju/haldet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "13B HalDet LLaVA checkpoint paired with the 7B repo.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10771, "dossier_id": "zju/haldet/haldet-7b", "name": "HalDet-LLaVA-7B", "maker": "zju", "family": "zju/haldet", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named HalDet LLaVA checkpoint. The name indicates hallucination detection. The task definition was not read from a card or paper.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10772, "dossier_id": "zju/instructbiomol/instructbiomol", "name": "InstructBioMol", "maker": "zju", "family": "zju/instructbiomol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10773, "dossier_id": "zju/instructcell/instructcell-chat", "name": "InstructCell-chat", "maker": "zju", "family": "zju/instructcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10774, "dossier_id": "zju/instructcell/instructcell-instruct", "name": "InstructCell-instruct", "maker": "zju", "family": "zju/instructcell", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10775, "dossier_id": "zju/instructprotein/instructprotein", "name": "InstructProtein", "maker": "zju", "family": "zju/instructprotein", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Protein instruction model from the same HICAI-ZJU lab as InstructBioMol. The card body was not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10776, "dossier_id": "zju/kgeditor/kgeditor", "name": "KGEditor", "maker": "zju", "family": "zju/kgeditor", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Knowledge-graph editing model released on the zjunlp Hub. The paper title was not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10777, "dossier_id": "zju/knowlm/knowlm-13b-base", "name": "KnowLM-13B-Base", "maker": "zju", "family": "zju/knowlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10778, "dossier_id": "zju/knowlm/knowlm-13b-ie", "name": "KnowLM-13B-IE", "maker": "zju", "family": "zju/knowlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10779, "dossier_id": "zju/knowlm/knowlm-13b-zhixi", "name": "KnowLM-13B-ZhiXi", "maker": "zju", "family": "zju/knowlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10780, "dossier_id": "zju/knowlm/knowlm-7b-base", "name": "KnowLM-7B-Base", "maker": "zju", "family": "zju/knowlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10781, "dossier_id": "zju/knowlm/knowlm-7b-chat", "name": "KnowLM-7B-Chat", "maker": "zju", "family": "zju/knowlm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10782, "dossier_id": "zju/knowrl/knowrl-deepseek-r1-distill-qwen-7b", "name": "KnowRL-DeepSeek-R1-Distill-Qwen-7B", "maker": "zju", "family": "zju/knowrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10783, "dossier_id": "zju/knowrl/knowrl-skywork-or1-7b-preview", "name": "KnowRL-Skywork-OR1-7B-Preview", "maker": "zju", "family": "zju/knowrl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Second published KnowRL base, from Skywork-OR1-7B-Preview. Skywork's own model is not re-catalogued here.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10784, "dossier_id": "zju/knowself/knowself", "name": "KnowSelf", "maker": "zju", "family": "zju/knowself", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10785, "dossier_id": "zju/labvla/labvla-5b", "name": "LabVLA-5B-Base", "maker": "zju", "family": "zju/labvla", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10786, "dossier_id": "zju/lightthinker/lightthinker-llama", "name": "LightThinker-Llama", "maker": "zju", "family": "zju/lightthinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Llama checkpoint of LightThinker. It compresses intermediate reasoning instead of keeping the full chain in context.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10787, "dossier_id": "zju/lightthinker/lightthinker-plus-llama", "name": "LightThinker-Plus-Llama", "maker": "zju", "family": "zju/lightthinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Llama checkpoint of LightThinker Plus.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10788, "dossier_id": "zju/lightthinker/lightthinker-plus-qwen", "name": "LightThinker-Plus-Qwen", "maker": "zju", "family": "zju/lightthinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Qwen checkpoint of LightThinker Plus. The card body was not opened beyond the family description on the 30B page.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10789, "dossier_id": "zju/lightthinker/lightthinker-plus-qwen3-30b-a3b", "name": "LightThinker-Plus-Qwen3-30B-A3B", "maker": "zju", "family": "zju/lightthinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10790, "dossier_id": "zju/lightthinker/lightthinker-qwen", "name": "LightThinker-Qwen", "maker": "zju", "family": "zju/lightthinker", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10791, "dossier_id": "zju/molgen/molgen-7b", "name": "MolGen-7B", "maker": "zju", "family": "zju/molgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10792, "dossier_id": "zju/molgen/molgen-large", "name": "MolGen-large", "maker": "zju", "family": "zju/molgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Earlier MolGen generator and its optimization checkpoint. They predate the 7B LLaMA MolGen on the Hub.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10793, "dossier_id": "zju/molinst/molinst-biotext", "name": "Mol-Instructions biotext", "maker": "zju", "family": "zju/molinst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomolecular instruction checkpoints released by zjunlp for the Mol-Instructions line. The paper title was not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10794, "dossier_id": "zju/molinst/molinst-molecule", "name": "Mol-Instructions molecule", "maker": "zju", "family": "zju/molinst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomolecular instruction checkpoints released by zjunlp for the Mol-Instructions line. The paper title was not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10795, "dossier_id": "zju/molinst/molinst-protein", "name": "Mol-Instructions protein", "maker": "zju", "family": "zju/molinst", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Biomolecular instruction checkpoints released by zjunlp for the Mol-Instructions line. The paper title was not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10796, "dossier_id": "zju/mt5-ie/mt5-ie", "name": "mT5-IE", "maker": "zju", "family": "zju/mt5-ie", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Early multilingual T5 extraction checkpoint from zjunlp, before the LLaMA KnowLM and OneKE models.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10797, "dossier_id": "zju/oceangpt/ocean-router", "name": "Ocean-router", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10798, "dossier_id": "zju/oceangpt/ocean-yolo", "name": "Ocean-YOLO", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Marine object detector shipped with the OceanGPT-X perception pieces. The card body was not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10799, "dossier_id": "zju/oceangpt/oceanclip-0-15b", "name": "OceanCLIP-0.15B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Small marine-species vision-language model in the OceanGPT-X stack. The card body was not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10800, "dossier_id": "zju/oceangpt/oceangpt-basic-14b", "name": "OceanGPT-basic-14B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Legacy larger OceanGPT text checkpoint from the July 2024 release, based on Qwen.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10801, "dossier_id": "zju/oceangpt/oceangpt-basic-2b", "name": "OceanGPT-basic-2B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smallest legacy OceanGPT text checkpoint, for local marine QA experiments.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10802, "dossier_id": "zju/oceangpt/oceangpt-basic-30b-a3b-instruct", "name": "OceanGPT-basic-30B-A3B-Instruct", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10803, "dossier_id": "zju/oceangpt/oceangpt-basic-30b-a3b-thinking", "name": "OceanGPT-basic-30B-A3B-Thinking", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Thinking variant of the recommended 30B-A3B OceanGPT. The README lists it separately from the instruct checkpoint.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10804, "dossier_id": "zju/oceangpt/oceangpt-basic-30b-oceanpile-sci", "name": "OceanGPT-basic-30B-OceanPile-Sci", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10805, "dossier_id": "zju/oceangpt/oceangpt-basic-4b-instruct", "name": "OceanGPT-basic-4B-Instruct", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Smaller recommended OceanGPT text model for marine question answering.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10806, "dossier_id": "zju/oceangpt/oceangpt-basic-4b-thinking", "name": "OceanGPT-basic-4B-Thinking", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Thinking variant of the recommended 4B OceanGPT.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10807, "dossier_id": "zju/oceangpt/oceangpt-basic-7b", "name": "OceanGPT-basic-7B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10808, "dossier_id": "zju/oceangpt/oceangpt-basic-8b", "name": "OceanGPT-basic-8B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Mid-size Qwen3 OceanGPT for marine text. The README uses it as the text inference example.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10809, "dossier_id": "zju/oceangpt/oceangpt-coder-0-6b", "name": "OceanGPT-coder-0.6B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "0.6B MOOS-code OceanGPT released on 2025-06-17.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10810, "dossier_id": "zju/oceangpt/oceangpt-coder-7b", "name": "OceanGPT-coder-7B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10811, "dossier_id": "zju/oceangpt/oceangpt-o-7b", "name": "OceanGPT-o-7B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Recommended multimodal OceanGPT for natural-language interpretation of certain sonar and marine-science images.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10812, "dossier_id": "zju/oceangpt/oceangpt-o-8b", "name": "OceanGPT-o-8B", "maker": "zju", "family": "zju/oceangpt", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Domain multimodal OceanPile snapshots for science, biology, and sonar. Cards were not opened.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10813, "dossier_id": "zju/onegen/onegen", "name": "OneGen", "maker": "zju", "family": "zju/onegen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Retrieval-generation checkpoints that keep generation and retrieval in one Llama-2-7B model. Three task repos were published.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10814, "dossier_id": "zju/onegen/onegen-embedding", "name": "OneGenEmbedding", "maker": "zju", "family": "zju/onegen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Embedding component published next to the OneGen chat checkpoints. What it embeds was not read.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10815, "dossier_id": "zju/oneke/oneke", "name": "OneKE", "maker": "zju", "family": "zju/oneke", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10816, "dossier_id": "zju/ontoprotein/ontoprotein", "name": "OntoProtein", "maker": "zju", "family": "zju/ontoprotein", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10817, "dossier_id": "zju/safedit/safedit-safety-classifier", "name": "SafeEdit Safety Classifier", "maker": "zju", "family": "zju/safedit", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10818, "dossier_id": "zju/token-mol/token-mol", "name": "Token-Mol 1.0", "maker": "zju", "family": "zju/token-mol", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10819, "dossier_id": "zju/wkm/wkm", "name": "WKM", "maker": "zju", "family": "zju/wkm", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "Named WKM agent weights for three interactive environments. The six repos are task and agent snapshots.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10820, "dossier_id": "zju/worfgen/worfgen", "name": "WorfGen-7B", "maker": "zju", "family": "zju/worfgen", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "WorfGen 7B weights on two bases. What WorF generates was not read from a card.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10821, "dossier_id": "zte/nebula/nebula-llm", "name": "\u661f\u4e91\u8bed\u8a00\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10822, "dossier_id": "zte/nebula/nebula-mm", "name": "\u661f\u4e91\u591a\u6a21\u6001\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10823, "dossier_id": "zte/nebula-cv/nebula-cv", "name": "\u661f\u4e91\u89c6\u89c9\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10824, "dossier_id": "zte/nebula-cv/nebula-cv-2-0", "name": "Nebula-CV2.0", "maker": "zte", "family": "zte/nebula-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10825, "dossier_id": "zte/nebula-cv/nebulacv-x", "name": "NebulaCV-X", "maker": "zte", "family": "zte/nebula-cv", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10826, "dossier_id": "zte/nebula-industry/nebula-city-lifeline", "name": "\u57ce\u5e02\u751f\u547d\u7ebf\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-industry", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10827, "dossier_id": "zte/nebula-industry/nebula-gov", "name": "\u661f\u4e91\u653f\u52a1\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-industry", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10828, "dossier_id": "zte/nebula-industry/nebula-industry", "name": "\u661f\u4e91\u5de5\u4e1a\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-industry", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10829, "dossier_id": "zte/nebula-industry/nebula-water", "name": "\u661f\u4e91\u6c34\u5229\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-industry", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10830, "dossier_id": "zte/nebula-telecom/nebula-signaling", "name": "\u7ed3\u6784\u5316\u4fe1\u4ee4\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-telecom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10831, "dossier_id": "zte/nebula-telecom/nebula-spatiotemporal", "name": "Spatio-Temporal Large Model", "maker": "zte", "family": "zte/nebula-telecom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10832, "dossier_id": "zte/nebula-telecom/nebula-telecom-structured", "name": "\u901a\u4fe1\u9886\u57df\u7ed3\u6784\u5316\u5927\u6a21\u578b", "maker": "zte", "family": "zte/nebula-telecom", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10833, "dossier_id": "zte/nebula-video/nebula-video", "name": "NebulaVideo", "maker": "zte", "family": "zte/nebula-video", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10834, "dossier_id": "zte/nebulacoder/nebulacoder", "name": "NebulaCoder", "maker": "zte", "family": "zte/nebulacoder", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10835, "dossier_id": "zte/ntele/ntele-r1-lite-32b-v2", "name": "NTele-R1-Lite-32B-v2", "maker": "zte", "family": "zte/ntele", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10836, "dossier_id": "zyphra/blackmamba/blackmamba-1-5b", "name": "BlackMamba 340M/1.5B", "maker": "zyphra", "family": "zyphra/blackmamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10837, "dossier_id": "zyphra/blackmamba/blackmamba-2-8b", "name": "BlackMamba 630M/2.8B", "maker": "zyphra", "family": "zyphra/blackmamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10838, "dossier_id": "zyphra/mamba/mamba-370m", "name": "Mamba-370M", "maker": "zyphra", "family": "zyphra/mamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10839, "dossier_id": "zyphra/zamba/zamba-7b-v1", "name": "Zamba-7B-v1", "maker": "zyphra", "family": "zyphra/zamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10840, "dossier_id": "zyphra/zamba/zamba-7b-v1-phase1", "name": "Zamba-7B-v1 phase 1", "maker": "zyphra", "family": "zyphra/zamba", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10841, "dossier_id": "zyphra/zamba2/zamba2-1-2b-instruct-v2", "name": "Zamba2-1.2B-Instruct-v2", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repo name and base-model tag mark it as a second instruction tune of the matching Zamba2 base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10842, "dossier_id": "zyphra/zamba2/zamba2-2-7b-instruct-v2", "name": "Zamba2-2.7B-Instruct-v2", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The repo name and base-model tag mark it as a second instruction tune of the matching Zamba2 base.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10843, "dossier_id": "zyphra/zamba2/zamba2-7b-instruct-v2", "name": "Zamba2-7B-Instruct-v2", "maker": "zyphra", "family": "zyphra/zamba2", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card presents v2 as a stronger instruction-following update of the 7B instruct model.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10844, "dossier_id": "zyphra/zamba2-vl/zamba2-vl-1-2b", "name": "Zamba2-VL-1.2B", "maker": "zyphra", "family": "zyphra/zamba2-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10845, "dossier_id": "zyphra/zamba2-vl/zamba2-vl-2-7b", "name": "Zamba2-VL-2.7B", "maker": "zyphra", "family": "zyphra/zamba2-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10846, "dossier_id": "zyphra/zamba2-vl/zamba2-vl-7b", "name": "Zamba2-VL-7B", "maker": "zyphra", "family": "zyphra/zamba2-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10847, "dossier_id": "zyphra/zaya/zaya1-74b-preview", "name": "ZAYA1-74B-Preview", "maker": "zyphra", "family": "zyphra/zaya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10848, "dossier_id": "zyphra/zaya/zaya1-8b-diffusion-preview", "name": "ZAYA1-8B-Diffusion-Preview", "maker": "zyphra", "family": "zyphra/zaya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "announced", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10849, "dossier_id": "zyphra/zaya/zaya1-base", "name": "ZAYA1-base", "maker": "zyphra", "family": "zyphra/zaya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10850, "dossier_id": "zyphra/zaya/zaya1-reasoning-base", "name": "ZAYA1-reasoning-base", "maker": "zyphra", "family": "zyphra/zaya", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10851, "dossier_id": "zyphra/zaya-vl/zaya1-vl-8b", "name": "ZAYA1-VL-8B", "maker": "zyphra", "family": "zyphra/zaya-vl", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "preview", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10852, "dossier_id": "zyphra/zonos/zonos-v0-1-hybrid", "name": "Zonos-v0.1 Hybrid", "maker": "zyphra", "family": "zyphra/zonos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.02, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10853, "dossier_id": "zyphra/zonos/zonos-v0-1-transformer", "name": "Zonos-v0.1 Transformer", "maker": "zyphra", "family": "zyphra/zonos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": 0.02, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10854, "dossier_id": "zyphra/zonos/zonos2", "name": "ZONOS2", "maker": "zyphra", "family": "zyphra/zonos", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "what_it_is_for": "The card says it is for expressive, low-latency speech and high-fidelity voice cloning across three language tiers.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10855, "dossier_id": "zyphra/zr1/zr1-1-5b", "name": "ZR1-1.5B", "maker": "zyphra", "family": "zyphra/zr1", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10856, "dossier_id": "zyphra/zuna/zuna", "name": "ZUNA", "maker": "zyphra", "family": "zyphra/zuna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10857, "dossier_id": "zyphra/zuna/zuna1-1", "name": "ZUNA1.1", "maker": "zyphra", "family": "zyphra/zuna", "context_length": null, "prompt_price_per_m": null, "completion_price_per_m": null, "status": "active", "weights": "open-weights", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": null, "coding_elo": null, "swe_bench": null, "humaneval": null, "twelve_cs_score": null, "speed_tps": null, "cosmos_tests": null, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } }, { "rank": 10858, "dossier_id": "rev/whisper/whisper-fusion", "name": "Whisper Fusion", "maker": "rev", "family": "rev/whisper", "context_length": null, "prompt_price_per_m": 0.005, "completion_price_per_m": null, "status": "deprecated", "weights": "closed", "empirical_score": null, "axes_scored": null, "cautious_projection": null, "high_projection": null, "lmsys_elo": -14998800, "coding_elo": -14998800, "swe_bench": -2499985.0, "humaneval": -1999930.0, "twelve_cs_score": 28.2, "speed_tps": 120.0, "cosmos_tests": 12, "is_free": false, "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.", "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.", "benchmarks": { "aa_intelligence_index": null, "aa_coding_index": null, "aa_agentic_index": null, "design_arena_elo": null, "math_500": null, "gpqa_diamond": null, "ifeval": null, "bfcl_tool_calling": null } } ] }